Exploiting Explicit and Implicit Feedback for Personalized Ranking

Abstract

The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset. Until now, nobody has studied personalized ranking algorithm by exploiting both explicit and implicit feedback. In order to overcome the defects of prior researches, a new personalized ranking algorithm (MERR_SVD++) based on the newest xCLiMF model and SVD++ algorithm was proposed, which exploited both explicit and implicit feedback simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR). Experimental results on practical datasets showed that our proposed algorithm outperformed existing personalized ranking algorithms over different evaluation metrics and that the running time of MERR_SVD++ showed a linear correlation with the number of rating. Because of its high precision and the good expansibility, MERR_SVD++ is suitable for processing big data and has wide application prospect in the field of internet information recommendation.