A New Approach For Rating Prediction by Using Trust Computation
Rui Qin, Nianlong Luo · 2019
With the advent of the era of big data, personalized recommendation is becoming more and more important. However, there is a problem of data sparsity in recommender system. The sparseness of data leads to the inaccuracy of recommendation. With the rapid development of social network, researchers begin to add social relationship into recommender system. Trust relationship is a very important kind of relationships. In this paper, we bring up a new approach for rating prediction by using trust computation. First, we describe a method for trust computation. Then, we give the strategy of prediction by using trust and user's rating preference. Finally, we compare our model with four related methods and conclude that our model can not only improve the prediction effect but also recommend more kinds of items accurately.