A Multidimensional Comprehensive Recommendation Method Based on Social Network

Abstract

The current entity recommendation based on social network is the research hotspot. Moreover, social network entity recommendation currently is one of the main problems of social network analysis. Recommendation system has been widely researched at present, and some typical recommendation methods are applied effectively in the practical application. However, the traditional recommendation methods based on social network have some problems, because they do not consider multiple dimensions to recommend entities. Aiming at these problems, this paper proposes a multi-dimensional comprehensive recommendation method based on social network. The core of this method mainly include entity similarity, user tightness, and user interest. In this paper, we introduce the calculation of user tightness, the calculation of user interest in a particular entity, the similarity calculation between entities. Further, we introduce the whole algorithm of multi-dimensional comprehensive the recommendation of related entities based on the users of social networks; Finally, in order to further confirm the effectiveness of the proposed algorithm, this paper compares the performance of this recommendation algorithm model with conditional recommendation model using the dataset of douban in the comprehensive experiment.