Abstract
Recommender systems typically produce a list of recommendations in one of two ways - through collaborative or content-based filtering. Collaborative filtering approaches build a model from a user’s past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users; then use that model to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined, and called Hybrid Recommender Systems. In this paper we present hybrid recommender system, which was used online during ACM RecSys 2013 Contest, where we were awarded 2nd best prize. The contest was based on the real data, which were provided by Yelp - US internet based business recommender.
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Nikulin, V. (2014). Hybrid Recommender System for Prediction of the Yelp Users Preferences. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_7
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DOI: https://doi.org/10.1007/978-3-319-08976-8_7
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