Collaborative recommendation algorithm based on probabilistic matrix factorization in probabilistic latent semantic analysis

In order to effectively solve the problem of new items and obviously improve the accuracy of the recommended results, we proposed a collaborative recommendation algorithm based on improved probabilistic latent semantic model in this paper, which introduces popularity factor into probabilistic latent semantic analysis to derive probabilistic matrix factorization model. The core idea is to integrate the semantic knowledge into the recommendation process to overcome the shortcomings of the traditional recommendation algorithm. We introduced popularity factor to form a quintuple vector so as to understand user preference, and can integrate the probabilistic matrix factorization to solve the problem of data sparsity on basis of Probabilistic Latent Semantic Analysis; then the probabilistic matrix factorization model is adopted to construct the weighted similarity function to compute the recommendation result. Experimental study on real-world data-sets demonstrates that our proposed method can outperform three state-of-the art methods in recommendation accuracy.