Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

Abstract

Similarity measures are crucial for electing neighbors for the users of recommender systems. However, the massive amount of the processed data in such applications may hide the inner nature of the utilized similarity measures. This paper devotes lots of studies to three standard similarity measures based on many synthetic and real datasets. The aim is to uncover the hidden nature of such measures and conclude their suitability for the recommender systems under different scenarios. Moreover, we propose a novel similarity measure called the normalized sum of multiplications (NSM) and two different variants of it. For experimentation, we examine all measures at three levels; a toy example, synthetic datasets, and real-world datasets. The results show that sometimes Pearson correlation coefficient and cosine similarity exclude similar neighbors in favor of less valuable ones. The former measures the correlation direction, while the latter measures the angle. However, both direction and angle are not similarity but an indication to it and can have the same values for two far vectors. On the other hand, the proposed similarity measure constantly reveals the exact similarity and tracks the closest neighbors. The results prove its robustness and its very good predictive accuracy compared to the traditional ones.