Abstract
Content-based recommender systems are widely used in different domains. However, they are usually inefficient to produce serendipitous recommendations. A recommendation is serendipitous if it is both relevant and unexpected. The literature indicates that one possibility of achieving serendipity in recommendations is to design them using partial similarities between items. From such intuition, coclustering can be explored to offer serendipitous recommendations to users. In this paper, we propose a coclustering-based approach to implement content-based recommendations. Experiments carried out on the MovieLens 2K dataset show that our approach is competitive in terms of serendipity.
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Notes
- 1.
Since \(\#\tau _i\) is independent of the rating qualification, a “bad” item can still be popular.
- 2.
Since similarities among movies and tags have already been treated in the coclustering process, the maximization of J would insert an overspecialization in the process. The threshold score (0.25) was obtained empirically from extensive tests.
- 3.
The original Movielens dataset is provided by GroupLens research group (http://www.grouplens.org). In this study, the following files were used: movies.dat, movie_tags.dat, tags.dat and user_ratedmovies-timestamps.dat.
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Silva, A.M., da Silva Costa, F.H., Diaz, A.K.R., Peres, S.M. (2018). Exploring Coclustering for Serendipity Improvement in Content-Based Recommendation. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_34
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