Link prediction in recommender systems based on vector similarity

Link prediction provides methods for estimating potential connections in complex networks that have theoretical and practical relevance for personalized recommendations and various other applications. Traditional collaborative filtering algorithms treat similarity as a scalar value causing some information loss. This paper is primarily a novel approach to calculating user similarity that uses a vector to measure user similarity across multiple dimensions based on the items’ characteristics. Our approach defines global similarity, local similarity and meta similarity to calculate vector similarity as indicators of similarity between users, revealing and measuring the difference between users’ general preferences in different scenarios. The experimental results show that the presented similarity methods improve prediction accuracy in recommender systems compared to some state-of-art approaches. Our results confirm that user similarity can be measured differently when considering different classes of items, which extends our understanding of similarity measurement.