Online Learning for Collaborative Filtering
Refereed conference paper presented and published in conference proceedings


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摘要Collaborative filtering (CF), aiming at predicting users' unknown preferences based on observational preferences from some users, has become one of the most successful methods to building recommender systems. Various approaches to CF have been proposed in this area, but seldom do they consider the dynamic scenarios: 1) new items arriving in the system, 2) new users joining the system; or 3) new rating updating the system are all dynamically obtained with respect to time. To capture these changes, in this paper, we develop an online learning framework for collaborative filtering. Specifically, we construct this framework consisting of two state-of-the-art matrix factorization based CF methods: the probabilistic matrix factorization and the top-one probability based ranking matrix factorization. Moreover, we demonstrate that the proposed online algorithms bring several attractive advantages: 1) they scale linearly with the number of observed ratings and the size of latent features; 2) they obviate the need to load all ratings in memory; 3) they can adapt to new ratings easily. Finally, we conduct a series of detailed experiments on real-world datasets to demonstrate the merits of the proposed online learning algorithms under various settings.
著者Ling G, Yang HQ, King I, Lyu MR
會議名稱IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI)
會議開始日10.06.2012
會議完結日15.06.2012
會議地點Brisbane
會議國家/地區澳大利亞
出版年份2012
月份1
日期1
出版社IEEE
電子國際標準書號978-1-4673-1490-9
國際標準期刊號1098-7576
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2020-27-10 於 00:09