HUNG CHAU's blog

Karen H. L. Tso-Sutter, Leandro Balby Marinho and Lars Schmidt-Thieme (SAC-08) http://dl.acm.org/citation.cfm?id=1364171 The paper “Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms” introduces an approach that allows to integrate tags in recommender systems in order to improve recommendation quality. Users’ rating information to items and content information of items are widely exploited in most of traditional recommender systems. Unlike attributes which are “global” descriptions of items, tags are “local” descriptions of items given by the users. Thus, tags could be interesting and useful information to enhance recommender system algorithms. The main goal of recommender systems is to predict items or ratings of items that users are interested in so that they can recommend items to the users. Metadata such as content information of items has typically been used as additional knowledge to improve the quality of recommendations. Attribute aware...

Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King ( CIKM-08 ) http://dl.acm.org/citation.cfm?id=1458205 In this paper, Hao Ma et al. present an approach that help to deal with very large datasets and users who have made few ratings in recommender systems. Traditional methods (e.g., Pearson Correlation Coefficient and Cosine) assume that two users have rated at least some items in common so that they can compute the similarity of these users. Thus, memory-based and model-based collaborative filtering algorithms fail to find similarities of users who have never rated any items. In daily life, people usually ask their family or friends for recommendations of new movies, songs, books, restaurants or places to visit. Therefore, the authors propose a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users’ social network information and rating records. Different with traditional persp...