A Hybrid Recommendation Algorithm Integrating Commodity Popularity and Trust

At present, collaborative filtering algorithm has been widely used in the recommendation system, but due to the data sparsity problem, the traditional collaborative filtering algorithm often has the drawback of low recommendation accuracy. This paper alleviates this problem by introducing users’ social trust network to mine users’ trust information. Besides, considering the contribution weight of popular items in the calculation of scoring similarity, this paper combines the influence of the proportion of users’ common scoring items into the traditional scoring similarity calculation formula. On this basis, this paper proposes a hybrid recommendation model (TPRA) of integrating commodity popularity and trust. Finally, the experimental results on Epinions data set show that the proposed algorithm can achieve better results than the comparison algorithm, the proposed algorithm can reduce MAE and RMSE by at least about 3%.