Affiliations: Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Correspondence:
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Corresponding author: Maria Brbić, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia. Tel.: +385 989031084; E-mail:[email protected]
Abstract: Machine learning algorithms are often used in content-based recommender
systems since a recommendation task can naturally be reduced to a
classification problem: A recommender needs to learn a classifier for a
given user where learning examples are characteristics of items previously
liked/bought/seen by the user. However, multi-valued and continuous
attributes require special approaches for classifier implementation as they
can significantly influence classifier accuracy. In this paper we propose
novel approaches for handling multi-valued and continuous attributes
adequate for the naïve Bayes classifier and decision trees classifier,
and tune it for content-based movie recommendation. We evaluate the
performance of the resulting approaches using the MovieLens data set
enriched with movie details retrieved from the Internet Movie Database. Our
empirical results demonstrate that the naïve Bayes classifier is more
suitable for content-based movie recommendation than the decision trees
algorithm. In addition, the naïve Bayes classifier achieves better
results with smart discretization of continuous attributes compared to the
approach which models continuous attributes with a Gaussian distribution.
Finally, we combine our best performing content-based algorithm with the
k-means clustering algorithm typically used for collaborative filtering, and
evaluate the performance of the resulting hybrid approach for a movie
recommendation task. The experimental results clearly show that the hybrid
approach significantly increases recommendation accuracy compared to
collaborative filtering while reducing the risk of over specification, which
is a typical problem of content-based approaches.
