结合全局与双重局部信息的社交推荐

Social Recommendation Combining Global and Dual Local Information

随着Web2.0的飞速发展,社交推荐逐渐成为推荐领域近几年的研究热点。如何更有效地利用用户的社交关系是社交推荐的关键,目前的社交推荐算法主要引入的是用户之间的直接联系(明确关系)。将社交关系进一步细分为明确关系和隐含关系,并结合历史评分得到的用户声誉信息刻画了由用户全局信息(声誉)与局部信息(明确关系和隐含关系)所构成的推荐系统框架。与现有的社交推荐算法相比,所提出的算法更全面地分析了用户的社交关系,且具有良好的可解释性。在Douban数据集和Epinions数据集上进行了实验,并将本算法与主流的推荐算法进行了比较,结果表明本算法具有更好的推荐精度。

With the rapid growth of Web2.0,social recommendation has become one of the hot research topics in the last few years.It is the key point to improve recommender systems using social contextual information in a more efficient way.The existing social recommendation approaches mainly take advantage of user＇s direct connection（explicit relation）.This paper detailed social relation as explicit relation and implicit relation and obtained the user＇s reputation by using his/her historic records.Then we proposed a recommendation framework capturing user＇s global social relation（reputation）and local social relation（explicit relation and implicit relation）.Using two real datasets,Douban and Epinions,we conducted a experimental study to investigate the performance of the proposed model GDLRec.We compared our approach with existing representative approaches.The results show that GDLRec outperforms other methods in terms of prediction accuracy.