Improving the Performance of Recommendation on Social Network by Investigating Interactions of Trust and Interest Similarity

On the social media, lots of people share their experiences through various factors like blogs, online ratings, reviews, online polling and tweets. Study shows that the factors such as interpersonal interest and interpersonal influence from the social media which is based on the circles as well as groups of friends leads to opportunities and challenges in solving the problems on datasets. This challenge is for the Recommender System (RS) to find the solution on cold start and sparsity problems. In this paper, on the basis of the probabilistic matrix factorization, the social factors like personal interest, interpersonal influence and interpersonal interest similarity are combined into a unified personalized recommendation model. These factors can improve the associating linkage in latent space. Various datasets are used to conduct the experiments to get the results that show that the proposed model performs better than the existing approaches