Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms
Proceedings of the 2008 ACM Symposium on Applied Computing, page 1995--1999. New York, NY, USA, ACM, (2008)DOI: 10.1145/1363686.1364171
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 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. 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.
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