关键词: 推荐系统, 协同过滤, 社会化协同排序, 隐式反馈, 显式反馈

Abstract: Abstract: The traditional social collaborative filtering algorithms based on rating prediction has the inherent deficiency in which the prediction value does not match the real sort, and social personalized ranking algorithms based on ranking prediction has close relationship with real industry problem settings. However, most existing social personalized ranking algorithms focus on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset. In order to exploit both the explicit and implicit influence of user trust and of item ratings and overcome the inherent deficiency of traditional social collaborative filtering algorithms based on rating prediction, a new social personalized ranking model (SPR_SVD++) based on the newest xCLiMF model and TrustSVD model was proposed, which exploited both the explicit and implicit influence of user trust and of item ratings simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR). Experimental results on practical datasets showed that our proposed model outperformed existing state-of-the-art TrustSVD, MERR_SVD++ and SVD++ algorithms over two different evaluation metrics (Normalized Discounted Cumulative Gain (NDCG) and ERR). Due to its high precision and good expansibility, SPR_SVD++ is suitable for processing big data and has wide application prospects in the field of internet information recommendation.

Key words: recommended systems, collaborative filtering, social personalized ranking, implicit feedback, explicit feedback