Solving the apparent diversity-accuracy dilemma of recommender systems

Details Solving the apparent diversity-accuracy dilemma of recommender systems Solving the apparent diversity-accuracy dilemma of recommender systems

2008-08-19

arxiv.org/abs/0808.2670v3

PNAS 107, 4511-4515, 2010

Computer Science

Information Retrieval

10 pages, 9 figures, 4 tables (final version with supporting information included)

Scientific paper

10.1073/pnas.1000488107

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.

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