Presentation | 2012-11-07 Recommendation with User Stereotyping Tatsuya OSAME, Jun SAKUMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Prediction accuracy of recommendation algorithm with user-item rating matrices can be improved by making use of user attributes as auxiliary information. However, attribute information, such as age and occupation, often takes discrete or categorical values. In this manuscript, we introduce user stereotyping method which allows to incorporate discrete or categorical user attribute information into recommendation algorithms. Stereotypes represent a group which contains relatively small number of users commonly having a few user attributes. Our method transforms user-item rating matrix into stereotype-item rating matrix. Thus, user attribute information is embedded into stereotype-item rating matrices. In the experiments, we present that prediction accuracy can be improved by user stereotyping. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | collaborative filtering / matrix factorization / recommendation system / stereotyping / hierarchical clustering |
Paper # | IBISML2012-63 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Recommendation with User Stereotyping |
Sub Title (in English) | |
Keyword(1) | collaborative filtering |
Keyword(2) | matrix factorization |
Keyword(3) | recommendation system |
Keyword(4) | stereotyping |
Keyword(5) | hierarchical clustering |
1st Author's Name | Tatsuya OSAME |
1st Author's Affiliation | Graduate School of Systems and Information Engineering, University of Tsukuba() |
2nd Author's Name | Jun SAKUMA |
2nd Author's Affiliation | Japan Science and Technology Agency |
Date | 2012-11-07 |
Paper # | IBISML2012-63 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 8 |
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