title: Solving the apparent diversity-accuracy dilemma of recommender systems.
abstract: 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 address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
authors.affiliation: Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou
authors.forename: Zoltán
authors.initials: Z
authors.lastname: Kuscsik
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Liu
authors.forename: Matús
authors.initials: M
authors.lastname: Medo
authors.forename: Joseph Rushton
authors.initials: JR
authors.lastname: Wakeling
authors.forename: Yi-Cheng
authors.initials: YC
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D016347:Medical Records Systems, Computerized; D015203:Reproducibility of Results; D014584:User-Computer Interface