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Université de Fribourg

Solving the apparent diversity-accuracy dilemma of recommender systems

Zhou, Tao ; Kuscsik, Zoltán ; Liu, Jian-Guo ; Medo, Matúš ; Wakeling, Joseph Rushton ; Zhang, Yi-Cheng

In: Proceedings of the National Academy of Sciences of the USA - PNAS, 2010, vol. 107, no. 10, p. 4511-4515

Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to...