A Probabilistic Model for Collaborative Filtering

被引：3
|
作者：
Lin, Zuoquan [1 ]
机构：
[1] Peking Univ, Sch Math Sci, Dept Informat Sci, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, MINING AND SEMANTICS (WIMS 2019) | 2019年
关键词：
recommender system; collaborative filtering; hidden Markov model; TIME; ALGORITHM;
D O I：
10.1145/3326467.3326472
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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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页数：8
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