Abstract
In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.
Highlights
Social networks and recommender system are quickly becoming popular
Collaborative relationships in recommender systems can be represented as a social network [2], the growth of social networks and the development of personalized recommendation techniques have improved users’ experiences and delivered higher quality of services [3]
The objective of this paper is to propose a new comprehensive similarity model to determine neighbors set and top-N items list recommended to the target user, thereby making a new contribution towards the solution of the data sparsity problem
Summary
Collaborative filtering is treated as a technique in assisting users to locate what they are interested in a timely manner [1]. Collaborative relationships in recommender systems can be represented as a social network [2], the growth of social networks and the development of personalized recommendation techniques have improved users’ experiences and delivered higher quality of services [3]. Social recommender systems are significantly challenged by the data sparsity issue -- the social network topology structure shows that only a small number of users have relatively many connections with other users, and most of the users have very few or no connections. Users sharing similar interests in social networks generally have a tendency to contact with each other [7]. Traditional personalized recommendation methods fail to take into account users’ social relationships and the fact that a user’s interests may be affected by another user’s interests through the social relationship, resulting in inferior recommendation quality
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