IU-PMF: Probabilistic Matrix Factorization Model Fused with Item Similarity and User Similarity

摘要

Probabilistic Matrix Factorization has been proven a very successful model for recommending because of scalability, accuracy and the ability to handle sparsity problem. However, many studies have demonstrated that PMF alone is poor to reveal local relationships which can be captured by neighborhood-aware methods. In this paper we present the IU-PMF model fusing Item Similarity and User Similarity in PMF, which combines the merits of both methods. The IU-PMF model consists of two phases: the Item and User similarity matrices computation phase not needing to be applied frequently; the fused PMF model solving phase which scales linearly with the number of observations. The IU-PMF model incorporates Item similarities and User similarities abstracted from User-Item ratings into the PMF model, which helps to overcome the often encountered problem of data sparsity, scalability and prediction quality. Experiments on three real-world datasets and the complexity analysis show that IU-PMF is scalable and outperforms several state-of-the-art methods.