Exploiting non-content preference attributes through hybrid recommendation method

Authors:
Fernando Mourão;Leonardo Rocha;Joseph A. Konstan;Wagner Meira, Jr.
Affiliations:
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;Universidade Federal de São João Del Rey, São João Del Rey, Brazil;University of Minnesota, Minneapolis, MN, USA;Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

Citing 14
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Abstract

This paper explores a method for incorporating into a recommender system explicit representations of user's preferences over non-content attributes such as popularity, recency, and similarity of recommended items. We show how such attributes can be modeled as a preference vector that can be used in a vector-space content-based recommender, and how that content-based recommender can be integrated with various collaborative filtering techniques through re-weighting of Top-M recommendations. We evaluate this approach on several recommender systems datasets and collaborative filtering methods, and find that incorporating the three preference attributes can lead to a substantial increase in Top-50 precision while also enhancing diversity and novelty.