基于社交网络上下文感知推荐算法.pdf

Software Engineering and Applications 软件工程与应用, 2015, 4(5), 101-113 Published Online October 2015 in Hans. /journal/sea /10.12677/sea.2015.45014 Context-Aware Recommendation Algorithm Based on Social Network Lei Chen, Gui Li, Zhengyu Li, Ziyang Han, Ping Sun Faculty of Information Control Engineering, Shenyang Jianzhu University, Shenyang Liaoning Email: cl090303009@163.com nd th th Received: Oct. 2 , 2015; accepted: Oct. 16 , 2015; published: Oct. 26 , 2015 Copyright © 2015 by authors and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). /licenses/by/4.0/ Abstract Context and social network information is very valuable for building accurate recommendation system. However, traditional recommendation systems could not combine different types of such information effectively to further improve the quality of recommendation. Therefore, we propose the context-aware recommendation algorithm based on social network SCRA (Social Network Based Context-Aware Recommendation Algorithm). For different types of context, we partition the rating matrix of initial user item by introducing random decision tree. In the leaf node of the tree, matrix factorization is used. Besides, we incorporate social network information by introducing Pearson Correlation Coefficient which contains context information to measure the similarity of users. To predict the rating of users for an item, we solve the objective function. Real datasets based experiments show that SCRA is better than the traditional recommendation algorithm in terms of precision. Keyword