Title :
An improved recommendation algorithm via social behaviors
Author :
Jin-Hu Liu;Yu-Xiao Zhu; Kun-Yu Shi
Author_Institution :
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, 611731, China
Abstract :
Recommendation is an important problem in the traditional field of data mining. As a consequence, various kinds of algorithms have been proposed in the last few years to improve the recommendation performance. However, many of them overlook users´ rating behaviors. In this paper, an improved recommendation algorithm with considering users´ habits and rating behaviors will be proposed. Firstly, calculate the ratings entropy for each user via users´ rating records which can reflect users´ behavioral features. Secondly, widely-used user-based method will be improved by considering rating entropy. Finally, the predicting rate is generated. Experimental results on the three datasets: Movie Lens, Netflix and RYM all suggest that the proposed method can enhance the algorithmic accuracy. Furthermore, since this method can improve those missing value more accurately via users´ similarity and rating behaviors which might shed some light on discovering users´ purchasing intention and optimizing the performance of recommender systems by human dynamics.
Keywords :
"Entropy","Recommender systems","Prediction algorithms","Algorithm design and analysis","Collaboration","Correlation"
Conference_Titel :
Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2015 12th International Computer Conference on
DOI :
10.1109/ICCWAMTIP.2015.7493910