A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model
Hongtao Yu · Journal of Information and Computational Science · 2015
The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user- and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy.