Papers › Solving the apparent diversity-accuracy dilemma of recommender systems
Solving the apparent diversity-accuracy dilemma of recommender systems
Tao Zhoua, Zoltán Kuscsika, Jian-Guo Liua, Matúš Medoa, Joseph Rushton Wakelinga, Yi-Cheng Zhanga
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 ob- tained 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 seman- tic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
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