A graph-based model to improve social trust and influence for social recommendation

Abstract

Social big data is large scale of data due to exponential popularity of social network and social media. Researchers can use social big data and social network for their observations if they analyse those in an intelligent manner. The target of intelligent decision is to find the most credible user in social network, who has the highest influence. A very large number of users are connected in social networks, implicitly friends-of-friends or explicitly mutual friends. They are able to communicate with each other and share their likes or dislikes on different topics. If users want to analyse any topic or purchase product like movie, book, they are populated with a lot of choices. Information overload due to large number of choices available to users limits effective product selection and hence results in reduced users’ satisfaction. Recommendation models are solution for providing better suggestion to users. Product’s recommendation at Amazon, friend’s recommendation at Facebook and music recommendation at iTunes are some of the popular examples of suggestions made on the basis of user’s interests. Recommendation models ease the user by reducing search space in social network graph. The main purpose of this paper is to improve social recommendations so that better and more appropriate choices are available for users. In this paper, an efficient technique for social recommendations using hyperedge and transitive closure is proposed. Social big data is processed and analysed in the form of social graphs. User–user and user–item connections are represented in the form of matrices. We have exploited homophily so that large number of connected users have trust on each other. Our model provides better recommendation to users by leveraging increased trust. The proposed model overcomes the limitations of traditional recommender systems like sparsity, cold start. It also improves prediction accuracy. The proposed model is evaluated through different metrics like MAE, precision, recall and RMSE. Empirical analysis shows significant improvement in recommendations. We have used Mahout library for improving recommendation accuracy and also handling large volume of data. SNAP library is also used for analysis of social big graphs. The proposed recommendation model is evaluated using Epinions and FilmTrust datasets. These datasets contain user’s ratings for various products in the scale of 1–5. Through analysis it is verified that the proposed model boosts the performance significantly. We have formulated recommendation model using manipulated social graph as per our proposed technique. This manipulated graph is mentioned as influence product graph (IPG) throughout this paper. IPG increases social trust value between connected users and this effect in recommending products in an effective and efficient manner.