| Presentation | 2007-06-29 Recommendation Method for Improving Customer Lifetime Value Tomoharu IWATA, Kazumi SAITO, Takeshi YAMADA, |
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| 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 |
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| Conference Information | |
| Committee | DE |
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| Conference Date | 2007/6/21(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Data Engineering (DE) |
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| 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 | |