Social recommendation model based on user interaction in complex social networks.

Yakun Li,Jiaomin Liu,Jiadong Ren,Floriana Gargiulo

doi:10.1371/journal.pone.0218957

Abstract

The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation services. However, the current research has rarely explored the impact of social interaction on recommendation performance, especially now that recommender systems face increasing challenges and suffer from poor efficiency due to social data overload. Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation. In this paper, we develop a novel social recommendation method based on the user interaction in complex social networks, called the SRUI model, to present a basis for improving the efficiency of the recommender systems. Specifically, a weighted social interaction network is first mapped to represent the interactions among social users according to the gathered information about historical user behavior. Thereafter, the complete path set is mined by the complete path mining (CPM) algorithm to find social similar neighbors with tastes similar to those of the target user. Finally, the social similar tendencies of the users on the complete paths are obtained to predict the final ratings of items through the SRUI model. A series of experimental results based on two real public datasets show that our approach performs better than other state-of-the-art methods in terms of recommendation performance.

Highlights

In order to evaluate the effectiveness of the proposed method and the recommendation results, several state-of-the-art algorithms were compared with our approach, including user-based collaborative filtering (UCF) [43], social network-based recommendation (SNR) [17], multiview user preference learning (MVUPL) [44], joint social and content recommendation (JSCR) [45] and graph-regularized matrix completion (GRMC) [46]
The results show that as the number of social similar neighbors (SSN) increases, the values of the mean absolute error (MAE) and root mean square error (RMSE) gradually decrease and the rating prediction becomes closer to the real value
The UCF method consistently performs the worst in our comparative experiments, and the proposed method performs significantly better than other advanced approaches based on the prediction accuracy

Summary

Introduction

“With the continuous innovation of Internet technology, new media has ushered in the era of ‘social plus’, which allows various users to communicate with each other and share resources, achieving multi-wins”, said Sina weibo chairman of Guowei Cao at the Fifth World Internet Conference held in Wuzhen, China. In contrast to the traditional research on text content [8], content recommendation processes in social platforms introduce content types that are created and shared by the users themselves, gather the feedback information of other users through the articulated relationships in complex networks, and implicitly infer the user preferences and content popularity [9,10] Another CF technique has been widely used in social systems [11], and the rationale behind it is to use the rating information of other users in social networks to find the neighbors who have similar tastes to the target user and recommend items to them. The last section ends the work by Social recommendation model based on user interaction in complex social networks summarizing the main contributions of the proposed approach and suggesting future research directions

Related work

Experiments and analysis

Baseline methods and evaluation metrics

Experimental results and analysis

Conclusion and future work