Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering
Implicit feedback collaborative filtering has attracted a lot of attention in collaborative filtering, which is called one class collaborative filtering (OCCF) However, the low recommendation accuracy and the high cost of previous methods impede its generalization in real scenarios. In this paper, we develop a new model named pairwise probabilistic matrix factorization (PPMF) by using the advantages of RankRLS. PPMF model takes RankRLS integrated with PMF (probabilistic matrix factorization) to learn the relative preference for items. Different from previous works, PPMF minimizes the average number of inversions in ranking rather than maximize the gaps of the binary predicted values for OCCF problem. Meanwhile, we propose to optimize the PPMF model by the pointwise stochastic gradient descent algorithm based on bootstrap sampling, which is more effective for parameter learning than the original optimization method used in previous works. Experiments on two datasets show that PPMF model achieves satisfactory performance and outperforms the state of the art implicit feedback collaborative ranking models by using different evaluation metrics.
Neurocomputing