To provide a recommendation device that reduces a calculation cost while considering the order of purchases, with high prediction accuracy.
The recommendation device 1 comprises: a preprocessing part 21 for generating input data 46 that has extracted purchase history for each user by using a user purchase history log 45; an extended Markov model estimation part 22 for estimating the prior probability that the user purchases the product from the input data 46 and a gap Markov model representing the probability that a specific product is purchased in the past when the user purchases a predetermined product; a weight estimation part 23 for building a coupled model that couples the prior probability 47 and the gap Markov model 48 by the maximum entropy principle, and estimating the weight representing unknown parameters; and a recommendation part 24 for selecting a product that shows the highest probability of purchase by the user, which is calculated from the coupled model using the input data 46, the prior probability 47, the gap Markov model 48, and the weight 49, and presenting it as a recommended object.
COPYRIGHT: (C)2009,JPO&INPIT
Samurai Yamada
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岩田 具治,購買順序を効率的に用いた協調フィルタリング,情報処理学会論文誌,日本,社団法人情報処理学会,2008年 3月15日,Vol.49,No.SIG4(TOM20),pages.125-134
岩田 具治,顧客生涯価値を高めるためのリコメンデーション法,電子情報通信学会技術研究報告,社団法人電子情報通信学会,2007年 6月21日,Vol.107,No.114,pages.57-62
Megumi Oishi
Shinji Nakamura