A Survey of Similarity Measures for Collaborative Filtering-Based Recommender System

Abstract

Recommendation system is one of the most valuable approaches to provide personalized services for users. It helps in finding the relevant information as per user’s interest from enormous amount of data. To achieve this, a similarity measure is used that computes the similarity between two users or items. There are multifarious methods to compute the similarity between users/items, but each method has some limitations. In this paper, we calculate the similarity between users through Pearson Correlation Coefficient (PCC), Cosine Correlation, Constrained Pearson Correlation Coefficient (CPCC), Sigmoid function-based Pearson Correlation Coefficient (SPCC), Jaccard similarity, and Minkowski distance measures (Euclidean distance and City block distance). The results show that Minkowski metric gives better result than other similarity calculation measures as it is less affected by size and sparsity of data set.