标题：DPMFNeg: A Dynamically Integrated Model for Collaborative Filtering
作者：Yang, Wenlong; Ma, Jun; Huang, Shanshan; Yang, Tongfeng
作者机构：[Yang, Wenlong; Ma, Jun; Huang, Shanshan; Yang, Tongfeng] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.
会议名称：16th Asia-Pacific Web Conference (APWeb)
会议日期：SEP 05-07, 2014
来源：WEB TECHNOLOGIES AND APPLICATIONS, APWEB 2014
关键词：Collaborative filtering; Recommender Systems; Neighborhood Approach;; Probabilistic Matrix Factorization
摘要：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.