Solving the apparent diversity-accuracy dilemma of recommender systems

Code

• github.com/rodfloripa/Heats_Probs_Recommender
  none★ 0

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 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.

Tasks

Reproductions