A Criterion Based on Fisher's Exact Test for Item Splitting in Context-Aware Recommender Systems

Item Splitting is a context-aware recommendation technique based on Collaborative Filtering (CF), which groups and exploits ratings according to the contexts in which they were generated. It shows positive effects on recommendation accuracy in the presence of significant differences between the users' preferences from distinct contexts. To determine whether such differences are significant, in this paper we propose a novel impurity criterion based on the Fisher's exact test, which returns a score on the difference between ratings given to an item. Experimental results on a dataset of movie ratings show a lower rating prediction error with respect to other impurity criteria - in particular, related with time context signals - , letting us improve the recommendation performance of a state-of-the-art CF algorithm in an offline evaluation setting that simulates real-world conditions.