Presentation 2007-06-29
Recommendation Method for Improving Customer Lifetime Value
Tomoharu IWATA, Kazumi SAITO, Takeshi YAMADA,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) It is important for online stores to improve Customer Lifetime Value(LTV) if they are to increase their profits. Conventional recommendation methods suggest items that best coincide with user's interests to maximize the purchase probability, and this does not necessarily help to improve LTV. We present a novel recommendation method that maximizes the-probability of the LTV being improved, which can apply to both of measured and subscription services. Our method finds frequent purchase patterns among high LTV users, and recommends items for a new user that simulate the found patterns. Using survival analysis techniques, we efficiently extract information from log data to find the patterns. Furthermore, we infer a user's interests from purchase histories based on maximum entropy models, and use these interests to improve the recommendations. Since a higher LTV is the result of greater user satisfaction, our method benefits users as well as online stores. We evaluate our method using two sets of real log data of a music distribution service.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Recommender System / Collaborative Filtering / Customer Lifetime Value / Maximum Entropy / Survival Analysis
Paper # DE2007-11,PRMU2007-37
Date of Issue

Conference Information
Committee DE
Conference Date 2007/6/21(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Recommendation Method for Improving Customer Lifetime Value
Sub Title (in English)
Keyword(1) Recommender System
Keyword(2) Collaborative Filtering
Keyword(3) Customer Lifetime Value
Keyword(4) Maximum Entropy
Keyword(5) Survival Analysis
1st Author's Name Tomoharu IWATA
1st Author's Affiliation NTT Corporation, NTT Communication Science Laboratories()
2nd Author's Name Kazumi SAITO
2nd Author's Affiliation NTT Corporation, NTT Communication Science Laboratories
3rd Author's Name Takeshi YAMADA
3rd Author's Affiliation NTT Corporation, NTT Communication Science Laboratories
Date 2007-06-29
Paper # DE2007-11,PRMU2007-37
Volume (vol) vol.107
Number (no) 114
Page pp.pp.-
#Pages 6
Date of Issue