DPMFNeg: A dynamically integrated model for collaborative filtering

Abstract

Collaborative Filtering (CF) techniques are the mostly applied methods in real world recommender systems. There are two typical types of CF, which are memory-based and model-based CF algorithms. However, these two CF methods in fact pay attention to different parts of ratings data. Memory-based CF methods are adept at finding local similar users, while model-based CF algorithms emphasize achieving global optimization. In this paper, we integrate a neighborhood approach and Probabilistic Matrix Factorization (PMF) into a hybrid CF model, DPMFNeg, which combines the advantages of memory-based and model-based CF algorithms. We explore the performance of our method on two test datasets - MoiveLens-100K and MoiveLens-1M. The results show that DPMFNeg performs better than other methods on those datasets in terms of MAE and RMSE. © 2014 Springer International Publishing Switzerland.