Presentation | 2006-05-18 A Collaborative Filtering Method based on Marginal Rating Distributions Shuhei KUWATA, Naonori UEDA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose a collaborative filtering method based on a probabilistic approach. In the proposed method, first empirical marginal distributions of the ratings over users and/or items are computed. Then, for unrated data, these distributions are represented as a function of unknown ratings, and the unknown ratings are predicted by minimizing KL divergence between both distributions. Unlike the conventional methods, the proposed method can predict all ratings simultaneously. In experiments using movie rating data, we confirmed that the proposed method could obtain significantly smaller prediction errors than the conventional probabilistic approach. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | collaborative filtering / marginal distribution / KL divergence / probabilistic approach |
Paper # | AI2006-3 |
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Conference Information | |
Committee | AI |
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Conference Date | 2006/5/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
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) | A Collaborative Filtering Method based on Marginal Rating Distributions |
Sub Title (in English) | |
Keyword(1) | collaborative filtering |
Keyword(2) | marginal distribution |
Keyword(3) | KL divergence |
Keyword(4) | probabilistic approach |
1st Author's Name | Shuhei KUWATA |
1st Author's Affiliation | NTT Comunication Science Laboratories, NTT Corporation() |
2nd Author's Name | Naonori UEDA |
2nd Author's Affiliation | NTT Comunication Science Laboratories, NTT Corporation |
Date | 2006-05-18 |
Paper # | AI2006-3 |
Volume (vol) | vol.106 |
Number (no) | 38 |
Page | pp.pp.- |
#Pages | 6 |
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