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Paper Abstract and Keywords
Presentation 2007-06-29 10:00
Recommendation Method for Improving Customer Lifetime Value
Tomoharu Iwata, Kazumi Saito, Takeshi Yamada (NTT) DE2007-11 PRMU2007-37
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) Recommender System / Collaborative Filtering / Customer Lifetime Value / Maximum Entropy Model / Survival Analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 107, no. 115, PRMU2007-37, pp. 57-62, June 2007.
Paper # PRMU2007-37 
Date of Issue 2007-06-21 (DE, PRMU) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF DE2007-11 PRMU2007-37

Conference Information
Committee PRMU DE  
Conference Date 2007-06-28 - 2007-06-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2007-06-PRMU-DE 
Language Japanese 
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 Model  
Keyword(5) Survival Analysis  
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Keyword(7)  
Keyword(8)  
1st Author's Name Tomoharu Iwata  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Kazumi Saito  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Takeshi Yamada  
3rd Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2007-06-29 10:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # DE2007-11, PRMU2007-37 
Volume (vol) vol.107 
Number (no) no.114(DE), no.115(PRMU) 
Page pp.57-62 
#Pages 6 
Date of Issue 2007-06-21 (DE, PRMU) 

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