Presentation 2007-05-31
Collaborative Filtering using Purchase Sequences
Tomoharu IWATA, Takeshi YAMADA, Naonori UEDA,
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Abstract(in English) We propose a collaborative filtering method that uses sequential information in purchase histories for recommendations. Markov models and maximum entropy models have been used for collaborative filtering problems that is the prediction of the next purchase item using the purchase history as input. In Markov models, their parameters can be estimated and updated fast, however their predictive accuracy is low. On the other hand, the accuracy of maximum entropy models is high, however high computational cost is required for their parameter estimation. We achieves the fast parameter estimation and high accuracy by combining multiple simple Markov models based on the maximum entropy principle. We show the validity of our method using real log data sets of music, movie and cartoon distribution services.
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Keyword(in English) recommendation / sequential data / maximum entropy model / use model
Paper # AI2007-3
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Committee AI
Conference Date 2007/5/24(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Collaborative Filtering using Purchase Sequences
Sub Title (in English)
Keyword(1) recommendation
Keyword(2) sequential data
Keyword(3) maximum entropy model
Keyword(4) use model
1st Author's Name Tomoharu IWATA
1st Author's Affiliation NTT Corporation, NTT Communication Science Laboratories()
2nd Author's Name Takeshi YAMADA
2nd Author's Affiliation NTT Corporation, NTT Communication Science Laboratories
3rd Author's Name Naonori UEDA
3rd Author's Affiliation NTT Corporation, NTT Communication Science Laboratories
Date 2007-05-31
Paper # AI2007-3
Volume (vol) vol.107
Number (no) 78
Page pp.pp.-
#Pages 6
Date of Issue