Cited By
View all- Liao XWu HWang Y(2020)Ant Collaborative Filtering Addressing Sparsity and Temporal EffectsIEEE Access10.1109/ACCESS.2020.29739318(32783-32791)Online publication date: 2020
A Probabilistic Model for Collaborative Filtering
Authors: 
Zuoquan Lin, Hanxuan ChenAuthors Info & Claims
Zuoquan Lin, Hanxuan ChenAuthors Info & ClaimsArticle No.: 25, Pages 1 - 8
Abstract
We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering system. It keeps updating every time it receives new data and has a constant limit of the time cost of every updating, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.
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Index Terms
- A Probabilistic Model for Collaborative Filtering
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Published In
June 2019
231 pages
ISBN:9781450361903
DOI:10.1145/3326467
- Conference Chair:
- Rajendra Akerkar,
- General Chair:
- Jason J. Jung
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- CAU: Chung-Ang University
- KISM: Korean Institute of Smart Media
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Published: 26 June 2019
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WIMS2019
WIMS2019: 9th International Conference on Web Intelligence, Mining and Semantics
June 26 - 28, 2019
Seoul, Republic of Korea
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View all- Liao XWu HWang Y(2020)Ant Collaborative Filtering Addressing Sparsity and Temporal EffectsIEEE Access10.1109/ACCESS.2020.29739318(32783-32791)Online publication date: 2020
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