A survey of collaborative filtering based social recommender systems
A survey of collaborative filtering based social recommender systems
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DOI：
10.1016/j.comcom.2013.06.009
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发表时间：
2014-03-15
影响因子：
6
通讯作者：
Steck, Harald
中科院分区：
文献类型：
--
作者：
Yang, Xiwang;Guo, Yang;Steck, Harald
推荐在我们的日常生活中扮演着越来越重要的角色。推荐系统自动地向用户推荐她可能感兴趣的项目。最近的研究表明，可以利用来自社交网络的信息来提高推荐的准确性。在本文中，我们提出了一个基于协同过滤（CF）的社会推荐系统的调查。我们提供了一个简要的概述推荐系统的任务和传统的方法，不使用社会网络信息。然后，我们提出了如何社交网络信息可以通过推荐系统作为额外的输入，以提高准确性。我们将基于CF的社会推荐系统分为两类：基于矩阵分解的社会推荐方法和基于邻域的社会推荐方法。对于每一类，我们调查和比较几个代表性的算法。(C)2013爱思唯尔有限公司版权所有。
Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several representative algorithms. (C) 2013 Elsevier B.V. All rights reserved.