Tag-aware recommender systems by fusion of collaborative filtering algorithms

Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tagaware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three twodimensional correlations and then applying a fusion method to re-associate these correlations. Addition...

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Karen H. L. Tso-Sutter, Leandro Balby Marinho, Lars Schmidt-Thieme
• Keywords: Algorithms, Applied Computing, CF Algorithms, SAC 2008, Tags