Technical Field

[001] The present disclosure generally relates to computerized systems and methods for providing dynamically generated product recommendation to an affiliate website. In particular, embodiments of the present disclosure relate to inventive and unconventional systems relate to providing dynamically generated product recommendation.

Background

[002] Targeting customers in the brick-and-mortar retail environment can be done with relative ease. Placing billboards in particular locations and targeting solicitations based on physical location, including direct mail and coupons, enables retailers to target their potential customers. Such targeting is different in the online shopping environment. With the increase in user Internet privacy laws, the impending end of third-party cookies, and awareness of user privacy on the internet, retailers face a problem particular to the online shopping environment: they have less access or no access to data often used to display content or results most relevant to a user. This data may include search history data, prior purchase data, or the similar data that can be used to track user habits or interests across the web.

[003] One approach is for retailers utilize is to serve content they assume will be relevant to a user based on heuristics or total number of units sold. For example, if there has been historically high demand or high interest for sweaters in the month of October, the retailer may advertise sweaters to users for which they have limited or no interest data. This content is then displayed on the retailer’s website or on a retailer’s affiliate website. One technical disadvantage with this approach is that the content is generated and selected manually, and therefore can result in lost revenue since the approach cannot monitor trending internet traffic and adjust content to

“flash” or transient trends, such as those found on social media. Another technical disadvantage is that the selection and generation of content is done manually and is not reasonably practicable by hand at a scale involving millions of products and thousands of affiliates.

[004] Therefore, there is a need for improved methods and systems for providing trend-aware content to affiliate websites where user interest information is limited or unavailable.

Summary

[005] One aspect of the present disclosure is directed to a method for providing a dynamically generated product recommendation to an affiliate website can include: generating a plurality of product features for each product in a product database, embedding product data associated with each product of the plurality of products by: encoding the product data in a vector, the vector can have a length associated with a quantity of product features; and determining, based on product engagement data, a numerical distance between at least two products, the numerical distance can represent a degree of association between the at least two products; determining, using a first machine learning model, candidate products based on the product engagement data; determining, using a second machine learning model, a selection of the candidate products based on at least the embedded product data and affiliate engagement data; and providing the selection of the candidate products to an affiliate server for display on an undiscernible user device.

[006] Yet another aspect of the present disclosure is directed to a computer- readable medium comprising a processor and memory containing instructions for providing a dynamically generated product recommendation to an affiliate website thereon, the instructions, when executed, causing the processor to: generate a plurality of product features for each product in a product database; embed product data associated with each product of the plurality of products by: encode the product data in a vector, the vector having a quantity of significant digits associated with a quantity of product features; and determine, based on product engagement data, a numerical distance between at least two products, the numerical distance representing a degree of association between the at least two products; determine, using a first machine learning, candidate products based on the product engagement data; determine, using a second machine learning, a selection of the candidate products based on at least the embedded product data and affiliate engagement data; and provide the selection of the candidate products to an affiliate server for display on an undiscernible user device.

[007] Yet another aspect of the present disclosure is directed to a computer- implemented system providing a dynamically generated product recommendation to an affiliate website comprising: a timer having an end condition that, when satisfied, is configured to cause a processor to: receive, from a product engagement database, product engagement data; receive, from an affiliate server, affiliate engagement data; and receive, from a product database, product data; a feature generation module configured to generate a plurality of product features for each product in a product database; an embedding engine configured to embed product data associated with each product of the plurality of products by: encoding the product data in a vector, the vector having a length associated with a quantity of product features; and determining, based on product engagement data, a numerical distance between at least two products, the numerical distance representing a degree of association between the at least two products; a candidate generation module configured to determine, using a first machine learning model, candidate products based at least on the product engagement data; and a recommendation engine configured to: determine, using a second machine learning model, a selection of the candidate products based on at least the embedded product data and affiliate engagement data; provide the selection of the candidate products to an affiliate server for display on an undiscernible user device; and initialize a second timer with a second end condition, and wherein the numerical distance is one of: a Euclidian distance or a Euclidian norm in a multi-dimensional Euclidian space, a cosine distance, or a coefficient representing a degree of association between the at least two products.

[008] Other systems, methods, and computer-readable media are also discussed herein.

Brief Description of the Drawings

[009] FIG. 1 A is a schematic block diagram illustrating an exemplary embodiment of a network comprising computerized systems for communications enabling shipping, transportation, and logistics operations, consistent with the disclosed embodiments.

[0010] FIG. 1 B depicts a sample Search Result Page (SRP) that includes one or more search results satisfying a search request along with interactive user interface elements, consistent with the disclosed embodiments.

[0011] FIG. 1 C depicts a sample Single Detail Page (SDP) that includes a product and information about the product along with interactive user interface elements, consistent with the disclosed embodiments. [0012] FIG. 1 D depicts a sample Cart page that includes items in a virtual shopping cart along with interactive user interface elements, consistent with the disclosed embodiments.

[0013] FIG. 1 E depicts a sample Order page that includes items from the virtual shopping cart along with information regarding purchase and shipping, along with interactive user interface elements, consistent with the disclosed embodiments.

[0014] FIG. 2 is a diagrammatic illustration of an exemplary fulfillment center configured to utilize disclosed computerized systems, consistent with the disclosed embodiments.

[0015] FIG. 3A is a schematic block diagram illustrating an exemplary embodiment of a network comprising a curation system for providing a dynamically generated product recommendation to an affiliate website, consistent with the disclosed embodiments.

[0016] FIG. 3B is a schematic block diagram illustrating an exemplary embodiment depicting dataflow between elements of FIG. 3A.

[0017] FIG. 4 is a flowchart for an exemplary method for providing a dynamically generated product recommendation to an affiliate website, consistent with the disclosed embodiments.

Detailed Description

[0018] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components and steps illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope of the invention is defined by the appended claims.

[0019] Embodiments of the present disclosure are directed to systems and methods configured for providing a dynamically generated product recommendation to an affiliate website.

[0020] Referring to FIG. 1 A, a schematic block diagram 100 illustrating an exemplary embodiment of a system comprising computerized systems for communications enabling shipping, transportation, and logistics operations is shown. As illustrated in FIG. 1 A, system 100 may include a variety of systems, each of which may be connected to one another via one or more networks. The systems may also be connected to one another via a direct connection, for example, using a cable. The depicted systems include a shipment authority technology (SAT) system 101 , an external front end system 103, an internal front end system 105, a transportation system 107, mobile devices 107A, 107B, and 107C, seller portal 109, shipment and order tracking (SOT) system 1 1 1 , fulfillment optimization (FO) system 1 13, fulfillment messaging gateway (FMG) 1 15, supply chain management (SCM) system 1 17, warehouse management system 1 19, mobile devices 1 19A, 1 19B, and 119C (depicted as being inside of fulfillment center (FC) 200), 3 ^rd party fulfillment systems 121 A, 121 B, and 121 C, fulfillment center authorization system (FC Auth) 123, and labor management system (LMS) 125.

[0021] SAT system 101 , in some embodiments, may be implemented as a computer system that monitors order status and delivery status. For example, SAT system 101 may determine whether an order is past its Promised Delivery Date (PDD) and may take appropriate action, including initiating a new order, reshipping the items in the non-delivered order, canceling the non-delivered order, initiating contact with the ordering customer, or the like. SAT system 101 may also monitor other data, including output (such as a number of packages shipped during a particular time period) and input (such as the number of empty cardboard boxes received for use in shipping). SAT system 101 may also act as a gateway between different devices in system 100, enabling communication (e.g., using store-and- forward or other techniques) between devices such as external front end system 103 and FO system 113.

[0022] External front end system 103, in some embodiments, may be implemented as a computer system that enables external users to interact with one or more systems in system 100. For example, in embodiments where system 100 enables the presentation of systems to enable users to place an order for an item, external front end system 103 may be implemented as a web server that receives search requests, presents item pages, and solicits payment information. For example, external front end system 103 may be implemented as a computer or computers running software such as the Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, or the like. In other embodiments, external front end system 103 may run custom web server software designed to receive and process requests from external devices (e.g., mobile device 102A or computer 102B), acquire information from databases and other data stores based on those requests, and provide responses to the received requests based on acquired information.

[0023] In some embodiments, external front end system 103 may include one or more of a web caching system, a database, a search system, or a payment system. In one aspect, external front end system 103 may comprise one or more of these systems, while in another aspect, external front end system 103 may comprise interfaces (e.g., server-to-server, database-to-database, or other network connections) connected to one or more of these systems.

[0024] An illustrative set of steps, illustrated by FIGS. 1 B, 1 C, 1 D, and 1 E, will help to describe some operations of external front end system 103. External front end system 103 may receive information from systems or devices in system 100 for presentation and/or display. For example, external front end system 103 may host or provide one or more web pages, including a Search Result Page (SRP) (e.g., FIG. 1 B), a Single Detail Page (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1 D), or an Order page (e.g., FIG. 1 E). A user device (e.g., using mobile device 102A or computer 102B) may navigate to external front end system 103 and request a search by entering information into a search box. External front end system 103 may request information from one or more systems in system 100. For example, external front end system 103 may request information from FO System 113 that satisfies the search request. External front end system 103 may also request and receive (from FO System 113) a Promised Delivery Date or “PDD” for each product included in the search results. The PDD, in some embodiments, may represent an estimate of when a package containing the product will arrive at the user’s desired location or a date by which the product is promised to be delivered at the user’s desired location if ordered within a particular period of time, for example, by the end of the day (11 :59 PM). (PDD is discussed further below with respect to FO System 113.)

[0025] External front end system 103 may prepare an SRP (e.g., FIG. 1 B) based on the information. The SRP may include information that satisfies the search request. For example, this may include pictures of products that satisfy the search request. The SRP may also include respective prices for each product, or information relating to enhanced delivery options for each product, PDD, weight, size, offers, discounts, or the like. External front end system 103 may send the SRP to the requesting user device (e.g., via a network).

[0026] A user device may then select a product from the SRP, e.g., by clicking or tapping a user interface, or using another input device, to select a product represented on the SRP. The user device may formulate a request for information on the selected product and send it to external front end system 103. In response, external front end system 103 may request information related to the selected product. For example, the information may include additional information beyond that presented for a product on the respective SRP. This could include, for example, shelf life, country of origin, weight, size, number of items in package, handling instructions, or other information about the product. The information could also include recommendations for similar products (based on, for example, big data and/or machine learning analysis of customers who bought this product and at least one other product), answers to frequently asked questions, reviews from customers, manufacturer information, pictures, or the like.

[0027] External front end system 103 may prepare an SDP (Single Detail Page) (e.g., FIG. 1 C) based on the received product information. The SDP may also include other interactive elements such as a “Buy Now” button, a “Add to Cart” button, a quantity field, a picture of the item, or the like. The SDP may further include a list of sellers that offer the product. The list may be ordered based on the price each seller offers such that the seller that offers to sell the product at the lowest price may be listed at the top. The list may also be ordered based on the seller ranking such that the highest ranked seller may be listed at the top. The seller ranking may be formulated based on multiple factors, including, for example, the seller’s past track record of meeting a promised PDD. External front end system 103 may deliver the SDP to the requesting user device (e.g., via a network).

[0028] The requesting user device may receive the SDP which lists the product information. Upon receiving the SDP, the user device may then interact with the SDP. For example, a user of the requesting user device may click or otherwise interact with a “Place in Cart” button on the SDP. This adds the product to a shopping cart associated with the user. The user device may transmit this request to add the product to the shopping cart to external front end system 103.

[0029] External front end system 103 may generate a Cart page (e.g., FIG.

1 D). The Cart page, in some embodiments, lists the products that the user has added to a virtual “shopping cart.” A user device may request the Cart page by clicking on or otherwise interacting with an icon on the SRP, SDP, or other pages. The Cart page may, in some embodiments, list all products that the user has added to the shopping cart, as well as information about the products in the cart such as a quantity of each product, a price for each product per item, a price for each product based on an associated quantity, information regarding PDD, a delivery method, a shipping cost, user interface elements for modifying the products in the shopping cart (e.g., deletion or modification of a quantity), options for ordering other product or setting up periodic delivery of products, options for setting up interest payments, user interface elements for proceeding to purchase, or the like. A user at a user device may click on or otherwise interact with a user interface element (e.g., a button that reads “Buy Now”) to initiate the purchase of the product in the shopping cart. Upon doing so, the user device may transmit this request to initiate the purchase to external front end system 103. [0030] External front end system 103 may generate an Order page (e.g., FIG.

1 E) in response to receiving the request to initiate a purchase. The Order page, in some embodiments, re-lists the items from the shopping cart and requests input of payment and shipping information. For example, the Order page may include a section requesting information about the purchaser of the items in the shopping cart (e.g., name, address, e-mail address, phone number), information about the recipient (e.g., name, address, phone number, delivery information), shipping information (e.g., speed/method of delivery and/or pickup), payment information (e.g., credit card, bank transfer, check, stored credit), user interface elements to request a cash receipt (e.g., for tax purposes), or the like. External front end system 103 may send the Order page to the user device.

[0031] The user device may enter information on the Order page and click or otherwise interact with a user interface element that sends the information to external front end system 103. From there, external front end system 103 may send the information to different systems in system 100 to enable the creation and processing of a new order with the products in the shopping cart.

[0032] In some embodiments, external front end system 103 may be further configured to enable sellers to transmit and receive information relating to orders.

[0033] Internal front end system 105, in some embodiments, may be implemented as a computer system that enables internal users (e.g., employees of an organization that owns, operates, or leases system 100) to interact with one or more systems in system 100. For example, in embodiments where system 100 enables the presentation of systems to enable users to place an order for an item, internal front end system 105 may be implemented as a web server that enables internal users to view diagnostic and statistical information about orders, modify item information, or review statistics relating to orders. For example, internal front end system 105 may be implemented as a computer or computers running software such as the Apache HTTP Server, Microsoft Internet Information Services (IIS), NGINX, or the like. In other embodiments, internal front end system 105 may run custom web server software designed to receive and process requests from systems or devices depicted in system 100 (as well as other devices not depicted), acquire information from databases and other data stores based on those requests, and provide responses to the received requests based on acquired information.

[0034] In some embodiments, internal front end system 105 may include one or more of a web caching system, a database, a search system, a payment system, an analytics system, an order monitoring system, or the like. In one aspect, internal front end system 105 may comprise one or more of these systems, while in another aspect, internal front end system 105 may comprise interfaces (e.g., server-to- server, database-to-database, or other network connections) connected to one or more of these systems.

[0035] Transportation system 107, in some embodiments, may be implemented as a computer system that enables communication between systems or devices in system 100 and mobile devices 107A-107C. Transportation system 107, in some embodiments, may receive information from one or more mobile devices 107A-107C (e.g., mobile phones, smart phones, PDAs, or the like). For example, in some embodiments, mobile devices 107A-107C may comprise devices operated by delivery workers. The delivery workers, who may be permanent, temporary, or shift employees, may utilize mobile devices 107A-107C to effect delivery of packages containing the products ordered by users. For example, to deliver a package, the delivery worker may receive a notification on a mobile device indicating which package to deliver and where to deliver it. Upon arriving at the delivery location, the delivery worker may locate the package (e.g., in the back of a truck or in a crate of packages), scan or otherwise capture data associated with an identifier on the package (e.g., a barcode, an image, a text string, an RFID tag, or the like) using the mobile device, and deliver the package (e.g., by leaving it at a front door, leaving it with a security guard, handing it to the recipient, or the like). In some embodiments, the delivery worker may capture photo(s) of the package and/or may obtain a signature using the mobile device. The mobile device may send information to transportation system 107 including information about the delivery, including, for example, time, date, GPS location, photo(s), an identifier associated with the delivery worker, an identifier associated with the mobile device, or the like. Transportation system 107 may store this information in a database (not pictured) for access by other systems in system 100. Transportation system 107 may, in some embodiments, use this information to prepare and send tracking data to other systems indicating the location of a particular package.

[0036] In some embodiments, certain users may use one kind of mobile device (e.g., permanent workers may use a specialized PDA with custom hardware such as a barcode scanner, stylus, and other devices) while other users may use other kinds of mobile devices (e.g., temporary or shift workers may utilize off-the- shelf mobile phones and/or smartphones).

[0037] In some embodiments, transportation system 107 may associate a user with each device. For example, transportation system 107 may store an association between a user (represented by, e.g., a user identifier, an employee identifier, or a phone number) and a mobile device (represented by, e.g., an International Mobile Equipment Identity (IMEI), an International Mobile Subscription Identifier (IMSI), a phone number, a Universal Unique Identifier (UUID), or a Globally Unique Identifier (GUID)). Transportation system 107 may use this association in conjunction with data received on deliveries to analyze data stored in the database in order to determine, among other things, a location of the worker, an efficiency of the worker, or a speed of the worker.

[0038] Seller portal 109, in some embodiments, may be implemented as a computer system that enables sellers or other external entities to electronically communicate with one or more systems in system 100. For example, a seller may utilize a computer system (not pictured) to upload or provide product information, order information, contact information, or the like, for products that the seller wishes to sell through system 100 using seller portal 109.

[0039] Shipment and order tracking system 111 , in some embodiments, may be implemented as a computer system that receives, stores, and forwards information regarding the location of packages containing products ordered by customers (e.g., by a user using devices 102A-102B). In some embodiments, shipment and order tracking system 111 may request or store information from web servers (not pictured) operated by shipping companies that deliver packages containing products ordered by customers.

[0040] In some embodiments, shipment and order tracking system 111 may request and store information from systems depicted in system 100. For example, shipment and order tracking system 111 may request information from transportation system 107. As discussed above, transportation system 107 may receive information from one or more mobile devices 107A-107C (e.g., mobile phones, smart phones, PDAs, or the like) that are associated with one or more of a user (e.g., a delivery worker) or a vehicle (e.g., a delivery truck). In some embodiments, shipment and order tracking system 111 may also request information from warehouse management system (WMS) 119 to determine the location of individual products inside of a fulfillment center (e.g., fulfillment center 200). Shipment and order tracking system 111 may request data from one or more of transportation system 107 or WMS 119, process it, and present it to a device (e.g., user devices 102A and 102B) upon request.

[0041] Fulfillment optimization (FO) system 113, in some embodiments, may be implemented as a computer system that stores information for customer orders from other systems (e.g., external front end system 103 and/or shipment and order tracking system 111). FO system 113 may also store information describing where particular items are held or stored. For example, certain items may be stored only in one fulfillment center, while certain other items may be stored in multiple fulfillment centers. In still other embodiments, certain fulfilment centers may be designed to store only a particular set of items (e.g., fresh produce or frozen products). FO system 113 stores this information as well as associated information (e.g., quantity, size, date of receipt, expiration date, etc.).

[0042] FO system 113 may also calculate a corresponding PDD (promised delivery date) for each product. The PDD, in some embodiments, may be based on one or more factors. For example, FO system 113 may calculate a PDD for a product based on a past demand for a product (e.g., how many times that product was ordered during a period of time), an expected demand for a product (e.g., how many customers are forecast to order the product during an upcoming period of time), a network-wide past demand indicating how many products were ordered during a period of time, a network-wide expected demand indicating how many products are expected to be ordered during an upcoming period of time, one or more counts of the product stored in each fulfillment center 200, which fulfillment center stores each product, expected or current orders for that product, or the like.

[0043] In some embodiments, FO system 113 may determine a PDD for each product on a periodic basis (e.g., hourly) and store it in a database for retrieval or sending to other systems (e.g., external front end system 103, SAT system 101 , shipment and order tracking system 111 ). In other embodiments, FO system 113 may receive electronic requests from one or more systems (e.g., external front end system 103, SAT system 101 , shipment and order tracking system 111 ) and calculate the PDD on demand.

[0044] Fulfilment messaging gateway (FMG) 115, in some embodiments, may be implemented as a computer system that receives a request or response in one format or protocol from one or more systems in system 100, such as FO system 113, converts it to another format or protocol, and forward it in the converted format or protocol to other systems, such as WMS 119 or 3 ^rd party fulfillment systems 121 A, 121 B, or 121 C, and vice versa.

[0045] Supply chain management (SCM) system 117, in some embodiments, may be implemented as a computer system that performs forecasting functions. For example, SCM system 117 may forecast a level of demand for a particular product based on, for example, based on a past demand for products, an expected demand for a product, a network-wide past demand, a network-wide expected demand, a count of products stored in each fulfillment center 200, expected or current orders for each product, or the like. In response to this forecasted level and the amount of each product across all fulfillment centers, SCM system 117 may generate one or more purchase orders to purchase and stock a sufficient quantity to satisfy the forecasted demand for a particular product. [0046] Warehouse management system (WMS) 119, in some embodiments, may be implemented as a computer system that monitors workflow. For example, WMS 119 may receive event data from individual devices (e.g., devices 107A-107C or 119A-119C) indicating discrete events. For example, WMS 119 may receive event data indicating the use of one of these devices to scan a package. As discussed below with respect to fulfillment center 200 and FIG. 2, during the fulfillment process, a package identifier (e.g., a barcode or RFID tag data) may be scanned or read by machines at particular stages (e.g., automated or handheld barcode scanners, RFID readers, high-speed cameras, devices such as tablet 119A, mobile device/PDA 119B, computer 119C, or the like). WMS 119 may store each event indicating a scan or a read of a package identifier in a corresponding database (not pictured) along with the package identifier, a time, date, location, user identifier, or other information, and may provide this information to other systems (e.g., shipment and order tracking system 111).

[0047] WMS 119, in some embodiments, may store information associating one or more devices (e.g., devices 107A-107C or 119A-119C) with one or more users associated with system 100. For example, in some situations, a user (such as a part- or full-time employee) may be associated with a mobile device in that the user owns the mobile device (e.g., the mobile device is a smartphone). In other situations, a user may be associated with a mobile device in that the user is temporarily in custody of the mobile device (e.g., the user checked the mobile device out at the start of the day, will use it during the day, and will return it at the end of the day).

[0048] WMS 119, in some embodiments, may maintain a work log for each user associated with system 100. For example, WMS 119 may store information associated with each employee, including any assigned processes (e.g., unloading trucks, picking items from a pick zone, rebin wall work, packing items), a user identifier, a location (e.g., a floor or zone in a fulfillment center 200), a number of units moved through the system by the employee (e.g., number of items picked, number of items packed), an identifier associated with a device (e.g., devices 119A- 119C), or the like. In some embodiments, WMS 119 may receive check-in and check-out information from a timekeeping system, such as a timekeeping system operated on a device 119A-119C.

[0049] 3 ^rd party fulfillment (3PL) systems 121A-121 C, in some embodiments, represent computer systems associated with third-party providers of logistics and products. For example, while some products are stored in fulfillment center 200 (as discussed below with respect to FIG. 2), other products may be stored off-site, may be produced on demand, or may be otherwise unavailable for storage in fulfillment center 200. 3PL systems 121 A-121 C may be configured to receive orders from FO system 113 (e.g., through FMG 115) and may provide products and/or services (e.g., delivery or installation) to customers directly. In some embodiments, one or more of 3PL systems 121A-121 C may be part of system 100, while in other embodiments, one or more of 3PL systems 121A-121 C may be outside of system 100 (e.g., owned or operated by a third-party provider).

[0050] Fulfillment Center Auth system (FC Auth) 123, in some embodiments, may be implemented as a computer system with a variety of functions. For example, in some embodiments, FC Auth 123 may act as a single-sign on (SSO) service for one or more other systems in system 100. For example, FC Auth 123 may enable a user to log in via internal front end system 105, determine that the user has similar privileges to access resources at shipment and order tracking system 111 , and enable the user to access those privileges without requiring a second log in process. FC Auth 123, in other embodiments, may enable users (e.g., employees) to associate themselves with a particular task. For example, some employees may not have an electronic device (such as devices 119A-119C) and may instead move from task to task, and zone to zone, within a fulfillment center 200, during the course of a day. FC Auth 123 may be configured to enable those employees to indicate what task they are performing and what zone they are in at different times of day.

[0051] Labor management system (LMS) 125, in some embodiments, may be implemented as a computer system that stores attendance and overtime information for employees (including full-time and part-time employees). For example, LMS 125 may receive information from FC Auth 123, WMS 119, devices 119A-119C, transportation system 107, and/or devices 107A-107C.

[0052] The particular configuration depicted in FIG. 1 A is an example only. For example, while FIG. 1 A depicts FC Auth system 123 connected to FO system 113, not all embodiments require this particular configuration. Indeed, in some embodiments, the systems in system 100 may be connected to one another through one or more public or private networks, including the Internet, an Intranet, a WAN (Wide-Area Network), a MAN (Metropolitan-Area Network), a wireless network compliant with the IEEE 802.11 a/b/g/n Standards, a leased line, or the like. In some embodiments, one or more of the systems in system 100 may be implemented as one or more virtual servers implemented at a data center, server farm, or the like.

[0053] FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is an example of a physical location that stores items for shipping to customers when ordered. Fulfillment center (FC) 200 may be divided into multiple zones, each of which are depicted in FIG. 2. These “zones,” in some embodiments, may be thought of as virtual divisions between different stages of a process of receiving items, storing the items, retrieving the items, and shipping the items. So while the “zones” are depicted in FIG. 2, other divisions of zones are possible, and the zones in FIG. 2 may be omitted, duplicated, or modified in some embodiments.

[0054] Inbound zone 203 represents an area of FC 200 where items are received from sellers who wish to sell products using system 100 from FIG. 1 A. For example, a seller may deliver items 202A and 202B using truck 201 . Item 202A may represent a single item large enough to occupy its own shipping pallet, while item 202B may represent a set of items that are stacked together on the same pallet to save space.

[0055] A worker will receive the items in inbound zone 203 and may optionally check the items for damage and correctness using a computer system (not pictured). For example, the worker may use a computer system to compare the quantity of items 202A and 202B to an ordered quantity of items. If the quantity does not match, that worker may refuse one or more of items 202A or 202B. If the quantity does match, the worker may move those items (using, e.g., a dolly, a handtruck, a forklift, or manually) to buffer zone 205. Buffer zone 205 may be a temporary storage area for items that are not currently needed in the picking zone, for example, because there is a high enough quantity of that item in the picking zone to satisfy forecasted demand. In some embodiments, forklifts 206 operate to move items around buffer zone 205 and between inbound zone 203 and drop zone 207. If there is a need for items 202A or 202B in the picking zone (e.g., because of forecasted demand), a forklift may move items 202A or 202B to drop zone 207.

[0056] Drop zone 207 may be an area of FC 200 that stores items before they are moved to picking zone 209. A worker assigned to the picking task (a “picker”) may approach items 202A and 202B in the picking zone, scan a barcode for the picking zone, and scan barcodes associated with items 202A and 202B using a mobile device (e.g., device 119B). The picker may then take the item to picking zone 209 (e.g., by placing it on a cart or carrying it).

[0057] Picking zone 209 may be an area of FC 200 where items 208 are stored on storage units 210. In some embodiments, storage units 210 may comprise one or more of physical shelving, bookshelves, boxes, totes, refrigerators, freezers, cold stores, or the like. In some embodiments, picking zone 209 may be organized into multiple floors. In some embodiments, workers or machines may move items into picking zone 209 in multiple ways, including, for example, a forklift, an elevator, a conveyor belt, a cart, a handtruck, a dolly, an automated robot or device, or manually. For example, a picker may place items 202A and 202B on a handtruck or cart in drop zone 207 and walk items 202A and 202B to picking zone 209.

[0058] A picker may receive an instruction to place (or “stow”) the items in particular spots in picking zone 209, such as a particular space on a storage unit 210. For example, a picker may scan item 202A using a mobile device (e.g., device 119B). The device may indicate where the picker should stow item 202A, for example, using a system that indicate an aisle, shelf, and location. The device may then prompt the picker to scan a barcode at that location before stowing item 202A in that location. The device may send (e.g., via a wireless network) data to a computer system such as WMS 119 in FIG. 1 A indicating that item 202A has been stowed at the location by the user using device 119B.

[0059] Once a user places an order, a picker may receive an instruction on device 119B to retrieve one or more items 208 from storage unit 210. The picker may retrieve item 208, scan a barcode on item 208, and place it on transport mechanism 214. While transport mechanism 214 is represented as a slide, in some embodiments, transport mechanism may be implemented as one or more of a conveyor belt, an elevator, a cart, a forklift, a handtruck, a dolly, or the like. Item 208 may then arrive at packing zone 211 .

[0060] Packing zone 211 may be an area of FC 200 where items are received from picking zone 209 and packed into boxes or bags for eventual shipping to customers. In packing zone 211 , a worker assigned to receiving items (a “rebin worker”) will receive item 208 from picking zone 209 and determine what order it corresponds to. For example, the rebin worker may use a device, such as computer 119C, to scan a barcode on item 208. Computer 119C may indicate visually which order item 208 is associated with. This may include, for example, a space or “cell” on a wall 216 that corresponds to an order. Once the order is complete (e.g., because the cell contains all items for the order), the rebin worker may indicate to a packing worker (or “packer”) that the order is complete. The packer may retrieve the items from the cell and place them in a box or bag for shipping. The packer may then send the box or bag to a hub zone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt, manually, or otherwise.

[0061] Hub zone 213 may be an area of FC 200 that receives all boxes or bags (“packages”) from packing zone 211 . Workers and/or machines in hub zone 213 may retrieve package 218 and determine which portion of a delivery area each package is intended to go to, and route the package to an appropriate camp zone 215. For example, if the delivery area has two smaller sub-areas, packages will go to one of two camp zones 215. In some embodiments, a worker or machine may scan a package (e.g., using one of devices 119A-119C) to determine its eventual destination. Routing the package to camp zone 215 may comprise, for example, determining a portion of a geographical area that the package is destined for (e.g., based on a postal code) and determining a camp zone 215 associated with the portion of the geographical area.

[0062] Camp zone 215, in some embodiments, may comprise one or more buildings, one or more physical spaces, or one or more areas, where packages are received from hub zone 213 for sorting into routes and/or sub-routes. In some embodiments, camp zone 215 is physically separate from FC 200 while in other embodiments camp zone 215 may form a part of FC 200.

[0063] Workers and/or machines in camp zone 215 may determine which route and/or sub-route a package 220 should be associated with, for example, based on a comparison of the destination to an existing route and/or sub-route, a calculation of workload for each route and/or sub-route, the time of day, a shipping method, the cost to ship the package 220, a PDD associated with the items in package 220, or the like. In some embodiments, a worker or machine may scan a package (e.g., using one of devices 119A-119C) to determine its eventual destination. Once package 220 is assigned to a particular route and/or sub-route, a worker and/or machine may move package 220 to be shipped. In exemplary FIG. 2, camp zone 215 includes a truck 222, a car 226, and delivery workers 224A and 224B. In some embodiments, truck 222 may be driven by delivery worker 224A, where delivery worker 224A is a full-time employee that delivers packages for FC 200 and truck 222 is owned, leased, or operated by the same company that owns, leases, or operates FC 200. In some embodiments, car 226 may be driven by delivery worker 224B, where delivery worker 224B is a “flex” or occasional worker that is delivering on an as-needed basis (e.g., seasonally). Car 226 may be owned, leased, or operated by delivery worker 224B. [0064] FIG. 3A and FIG. 3B are schematic block diagrams illustrating an exemplary embodiment of a network comprising a curation system 300 for providing a dynamically generated product recommendation to an affiliate website, consistent with the disclosed embodiments.

[0065] Curation system 300 can include memory 302, processor 304, a candidate generation module 306, an encoding module 308, feature generation module 310, and a curation module 312. Additionally, the curation system 300 can include a timer 314. The curation system 300 can be in communication with other aspects of system 100, for example, internal front end system 105 or external front end system 103. Curation system 300 and/or aspects of the curation system 300 may be configured to send and/or receive data from one or more external servers, for example, an affiliate server 316 over a network. Curation system 300 and/or aspects of the curation system 300 can be configured to send and/or receive data from aspects of system 100. Curation system 300 and/or aspects of the curation system 300 can be configured to send and/or receive data from one or more external devices (e.g., mobile device 102a and/or computer 102b). For example, curation system 300 may transmit data to the affiliate server 316 for display on a user device. In another example, curation system 300 may receive data from the affiliate server 316. In some examples, curation system 300 can be external to system 100. In some example, curation system 300 can be associated with system 100.

[0066] One or more external devices (e.g., mobile device 102a, and/or computer 102b) may be undiscernible user devices. An undiscernible user device may refer to user devices for which there is no record of prior engagement with the affiliate server or an external front end system. For example, the undiscernible user device does not have a cookie or other trackers indicating a prior visit to the affiliate server 316, thereby limiting access to or excluding information required by conventional product recommendation systems to provide the user device with personalized product recommendations. In some examples, the undiscernible user device does not have a cookie or other trackers indicating a prior visit to the external front end system 102, thereby limiting or excluding information required by conventional product recommendation systems to provide the user device with personalized product recommendations. In some examples, the undiscernible user device may not be logged into one or more accounts associated with either an affiliate or of the owner of system 100, thereby limiting access to or excluding information required by conventional product recommendation systems to provide the user device with personalized product recommendations. This creates the technical problem of determining and transmitting data associated products a user is most likely to engage with, without prior knowledge of the user’s preferences.

[0067] The processor 304 may include processors used in desktops, mobile devices, and or/ servers. Processor 304 may include, for example, x86, x86-64, ARM, and/or RISC architecture processors. Processor 304 may be a single core or multicore processors. In an example, processor 304 may be a system-on-chip (SoC). Memory 302 may be non-transitory computer readable storage medium, for example, a floppy disk, SSD, HDD, Zip drive, USB flash drive, optical disks, or SD card. Memory 302 may contain instructions thereon, the instructions, when executed, may cause one or more processors of system 100 to execute one or more methods disclosed herein. In some embodiments, memory 302 can also store one or more databases, for example, a product database comprising product data, product engagement database including product engagement data, embedding database including embedded product data (e.g., encoded product data), and/or candidate database including candidate product data. In some embodiments, the one or more databases can be stored in a distributed manner within system 100 and/or affiliate server 316. The embedded product data may include at least a portion of the product database, wherein each product within the database represented by a vector.

[0068] The candidate generation module 306 can be in communication with one or more aspects of curation system 300. The candidate generation module 306 can be configured to determine candidate products based on the product engagement data. Candidate products may refer to products a subset or selection of all products from a product database that are above a certain threshold in a ranked list. Product engagement data can include at least one of: search session data, website session data, or application session data. The encoding module 308 can embed product data associated with each product of the plurality of products by encoding the product data in a vector. The encoding module 308 can include one or more machine learning models to encode the product data. One or more machine learning models can include Regressions, Classification, Naive Bayesian Model, Random Forest Model, Neural Networks, Support Vector Machines, DeepCTR, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Radial Basis Function Networks (RBFNs), Multilayer Perceptrons (MLPs), Self Organizing Maps (SOMs), Deep Belief Networks (DBNs), Restricted Boltzmann Machines( RBMs), and/or Autoencoders.

[0069] The feature generation module 310 can be configured to generate a plurality of product features for each product in a product database. Product features can include at least one of: product reviews, click data for searched products, click data for displayed product advertisements, conversion data for searched products, conversion data for displayed product advertisements, product sales data, or embedded product data. Embedded product data may include at least one of: product features may include one or more attributes about a physical or digital product, for example, size, shape, color, or other descriptors commonly or uncommonly associated with a product or product category and used to distinguish one product from another (or one product category from another). It will be understood that multiple products or product categories may be used to generate product features.

[0070] The curation module 312 can be configured to determine a selection of the candidate products. The curation module 312 may include a one or more machine learning models, for example, a trained deep learning click-through-rate model, to determine a selection of the candidate product data. One or more machine learning models can include Regressions, Classification, Naive Bayesian Model, Random Forest Model, Neural Networks, Support Vector Machines, DeepCTR, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Radial Basis Function Networks (RBFNs), Multilayer Perceptrons (MLPs), Self Organizing Maps (SOMs), Deep Belief Networks (DBNs), Restricted Boltzmann Machines( RBMs), and/or Autoencoders.

[0071] The selection of the candidate products can be based on the embedded product data and/or the affiliate engagement data. Affiliate engagement data can include at least one of: information associated with products sold on an affiliate’s website or application, information associated with products clicked on an affiliate’s website or application Additionally or alternatively, the curation module 312 can be configured to provide the selection of the candidate products to an affiliate server 316 for display on an undiscernible user device.

[0072] In some examples, determining the selection of candidate products may include re-ranking the candidate products using additional data and a second machine learning model, and selecting a selection of the candidate products above a threshold based on the re-ranked list.

[0073] The timer 314 can be a virtual timer (e.g., implemented in software). The timer 314 can be initialized by a user or upon successful execution of instructions by one or more processors. Additionally or alternatively, the timer 314 can be satisfied upon successful execution of instructions by one or more processors, or by reaching a predetermined value. Timer 314 may be a trigger to cause execution of instructions by one or more processors. The timer 314 may trigger on a predetermined schedule, for example, every 24 hours. In some embodiments, the timer may trigger every 3 hours, 6 hours, 12 hours, 18 hours, 36 hours, or 48 hours. In some embodiments, the timer 314 may trigger every day, every other day, once a week, once a fortnight, or once a month. In some examples, when timer 314 triggers, the product engagement database, product database and/or affiliate engagement database may update, and cause the execution of method 400, discussed below. Timer 314 may then reset to follow the predetermined schedule.

[0074] Affiliate server 316 can include processor 318, memory 320, and affiliate website 322. The affiliate server 316 can be in communication with external devices (e.g., mobile device 102A and/or computer 102B), external front end system 103, internal front end system 105, and/or an curation system 300. [0075] The processor 318 may be similar to processor 304 discussed above. Memory 320 may be similar to memory 302 discussed above.

[0076] The affiliate website 322 can include at least one advertisement. In some examples, the at least one advertisement can be associated with the selection of candidate products. In some examples, the at least one advertisement can be at least one among the selection of candidate products. In some embodiments, the affiliate website 322 can collect and store engagement data in an affiliate engagement database. In some embodiments, affiliate website 322 can include features. Features can include, for example, content category (e.g. blogs, news, ecommerce, or the like), content type (e.g. text only, video, audio, article, or the like), and/or content keyword (e.g. jackets, swimwear, moms, babies, or the like). Feature may also include ad spot related features, for example, image size (e.g. 250x50, 150x150, or the like) and/or placement (e.g. top, bottom, floating, pop-up, or the like). In some examples, technical aspects of the affiliate website 322 may be similar to external front end system 102 discussed earlier.

[0077] Fig. 4 is a flowchart for an exemplary method for providing a dynamically generated product recommendation to an affiliate website, consistent with the disclosed embodiments. Method 400 may begin at block 402. In block 402, the curation system 300 may generate a plurality of product features. The generation of product features may include collecting data associated with a product. The data is then enumerated to determine a quantity of significant digits for the encoding process.

[0078] In block 404, the curation system 300 can encode the product data in a vector. In some examples, the curation system 300 may encode the product data in a vector using a machine learning model. In some examples, the encoding module 306 may encode the product data into a vector using a machine learning model. The vector can have a length associated with a quantity of product features. For example, an ink pen may have one or more product features, such as ink color, and nib size. A blue 0.35mm ink pen may be encoded or embedded as vector [1 , 1 , 0], whereas a red 0.7mm ink pen may be encoded as a vector [1 ,-1 , 0.5], where the first number from the left is a product category (e.g., ink pen), the second number from the left may represent a first product feature (e.g., ink color), and the last number may represent a second product feature (e.g., nib size). It will be understood that the more product categories may be represented by increasing the length of the vector, increasing the precision of the values that represent product categories, or both. In some embodiments, each product may be represented by a vector of coordinates, where the length of the vector corresponds to the quantity of product features and the value of each coordinate corresponds to the inclusion of a product feature. Similarly, it will be understood that more product features may be represent by including additional significant digits or coordinates. For example, a large lavender scented candle may be represented by [ 3, 9, 5], where the product category is 3, the candle size (e.g., large) is encoded as 9 and the lavender scent is encoded as 5.

[0079] In block 406, the curation system 300 can determine a numerical distance between at least two products. The numerical distance can represent a degree of association between the at least two products. Determining a numerical distance between two products may include determining a Euclidian distance in a multi-dimensional Euclidian space, a Euclidian norm in a multi-dimensional Euclidian space, a cosine distance, or a coefficient representing a degree of association between the at least two products. For example, and continuing the ink pen example above, determining a Euclidian distance may include subtracting [1 , 1 , 0] from [ 1 , 1 , 0.5] resulting in 0.5. A smaller Euclidean distance may be associated with a higher degree of association between two or more products. In some examples, and continuing the ink pen example above, determining a Euclidian norm may include subtracting [1 , 1 , 0] from [1 , 1 , 0.5], then taking the modulus (or absolute value) of the result. A smaller Euclidean norm may be associated with a higher degree of association between two or more products. In some examples, and continuing the ink pen example above, determining a cosine similarity may include multiplying [1 , 1 , 0] and [1 , 1 , 0.5], then dividing the result by the cross-product of the lengths of each of the products. A larger positive cosine similarity value may be associated with a higher degree of association between two or more products. In some examples, and continuing the ink pen example above, determining a coefficient representing a degree of association between the red ink pen and the blue ink pen may include a coefficient of 1 for a high degree of similarity, and 0 for a low degree of similarity between the two products. One of skill would understand finding a numerical distance, such as those described above, between two or more products represented by coordinates in a multi-dimensional Euclidean space.

[0080] In block 408, the curation system 300 can determine, using a machine learning model, candidate products based on the product engagement data. In some embodiments, the curation system 300 can generate a ranked list of products based on affiliate engagement data and/or product engagement data. In some embodiments, the ranked list can be ordered by top selling products or top searched products, as identified by the product engagement data. Additionally or alternatively, the ranked list can be ordered by top selling product from an affiliate website or top clicked product on an affiliate website. In some embodiments, the ranked list can be ordered by products most frequently bought together with top selling or top searched products. Additionally or alternatively, the ranked list can be ordered by products most frequently bought together with top selling product from an affiliate website or top clicked product on an affiliate website. A subset or selection of the ranked list of products from a product database above a threshold may be selected. Example threshold can include, top 5 products, top 10 products, top 25 products, top 50 products, top 75 products, and/or top 100 products. Larger or smaller thresholds are contemplated. The subset of products may be referred to as “candidate products”.

[0081] In block 410, the curation system 300 can determine, using a machine learning model, a selection of the candidate products. The curation system 300 may re-rank the candidate products at least based on affiliate website data. For example, if the affiliate website is a cooking blog, then candidate products associated with a recipe’s ingredients or cookware may be re-ranked higher. Additionally or alternatively, the curation system 300 may re-rank the candidate products based on product features. For example, a product feature may include the product’s rating, and high rated products may be re-ranked above lower rated products. A subset or selection of the re-ranked list of the candidate products above a threshold may be selected. Example threshold can include, top 5 products, top 10 products, top 25 products, top 50 products, top 75 products, and/or top 100 products. Larger or smaller thresholds are contemplated. In some examples, products with higher click- through-rates (CTR) are re-ranked higher. In some examples, products with higher conversion rates (CVR) are re-ranked higher. In some examples, products with similar price points to previously purchased products from the affiliate website are reranked higher.

[0082] In block 412, the curation system 300 can provide the selection of the candidate products to an affiliate server for display on an undiscernible user device. The curation system 300 or aspects of system 100 may transmit the selection of the candidate products to an affiliate server 316 for display on the affiliate’s website 322 to users visiting the affiliate’s website 322 using undiscernible user devices.

[0083] While the present disclosure has been shown and described with reference to particular embodiments thereof, it will be understood that the present disclosure can be practiced, without modification, in other environments. The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive media.

[0084] Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. Various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets.

[0085] Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.