Paper Info
Title
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Abstract
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 tag-aware 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 two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.
Year
DOI
Venue
2008
10.1145/1363686.1364171
SAC
Keywords
Field
DocType
additional knowledge,tags information,different cf algorithm,content information,fusion method,tag-aware recommender system,tag-aware rs,useful information,standard cf algorithm,cf algorithm,rs algorithm,recommender systems,recommender system,three dimensional,collaborative filtering
Recommender system,Data mining,Metadata,Collaborative filtering,Information retrieval,Computer science,Popularity,Algorithm
Conference
Citations 
PageRank 
References 
188
6.07
15
Authors
3
Search Limit
100188
Name
Order
Citations
PageRank
Karen H. L. Tso-Sutter12037.20
Leandro Balby Marinho270235.57
Lars Schmidt-Thieme33802216.58