关键词: 推荐系统, 协同过滤, 隐语义模型, 均方根误差

Abstract: As one of the most successful approaches to building recommender systems, Collaborative Filtering（CF） uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. The two successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. This paper introduces some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data, and the results are better than those previously published on that dataset.

Key words: recommender systems, Collaborative Filtering（CF）, latent factor model, Root Mean Square Error（RMSE）