An entropy-based neighbor selection approach for collaborative filtering

Collaborative filtering is an emerging technology to deal with information overload problem guiding customers by offering recommendations on products of possible interest. Forming neighborhood of a user/item is the crucial part of the recommendation process. Traditional collaborative filtering algorithms solely utilize entity similarities in order to form neighborhoods. In this paper, we introduce a novel entropy-based neighbor selection approach which focuses on measuring uncertainty of entity vectors. Such uncertainty can be interpreted as how a user perceives rating domain to distinguish her tastes or diversification of items' rating distributions. The proposed method takes similarities into account along with such uncertainty values and it solves the optimization problem of gathering the most similar entities with minimum entropy difference within a neighborhood. Described optimization problem can be considered as combinatorial optimization and it is similar to 0-1 knapsack problem. We perform benchmark data sets-based experiments in order to compare our method's accuracy with the conventional user- and item-based collaborative filtering algorithms. We also investigate integration of our method with some of previously introduced studies. Empirical outcomes substantiate that the proposed method significantly improves recommendation accuracy of traditional collaborative filtering algorithms and it is possible to combine the entropy-based method with other compatible works introducing new similarity measures or novel neighbor selection methodologies.