Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks

Collaborative filtering (CF) is one of the most popular recommendation methods, and the co-rating-based similarity measurement is widely used in CF for predicting ratings of unfamiliar items. In addition to rating information, social trust has now been considered useful in collaborative recommendations. In this work, we present a hybrid approach that combines user ratings and social trust for making better recommendations. In contrast to other trust-aware recommendation works, our approach exploits distrust links and investigates their propagation effects. In addition, our approach combines the k-nearest neighbors and the matrix factorization methods to maximize the advantages of both rating and trust information. Several series of experiments are conducted, in which different types of social trust are incrementally included to evaluate the presented approach. The results show that distrust information is beneficial in ratings prediction, and the developed hybrid approach can effectively enhance the recommendation performance.