A Novel Recommendation Algorithm Based on Heterogeneous Information Network Similarity and Preference Diffusion

Recommender system has been proposed as a key tool to overcome the problem of information overload. In the present era of big data, how to utilization the side information of users, items is a new challenge. This paper put forward a novel solution based on the heterogeneous information network and preference diffusion. The similarity matrices of users and items are initially computed based on meta-path similarity algorithm; three new preference diffusion methods has been proposed to fuse the similarity matrix and the user-item rating matrix; finally uses the traditional recommendation techniques based on matrix factorization to predict the results. With the experiment in a classical data set MovieLens 100 K and the movie attributes extended from IMDb, verifies the effectiveness of the solution that with heterogeneous information network to make full use of users and item attributes information and the preference diffusion with rating matrix can improve the recommendation accuracy effectively.