Presentation | 2007-05-31 Collaborative Filtering using Purchase Sequences Tomoharu IWATA, Takeshi YAMADA, Naonori UEDA, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | recommendation / sequential data / maximum entropy model / use model |
Paper # | AI2007-3 |
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Committee | AI |
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Conference Date | 2007/5/24(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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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 |