Recommendation with Social Dimensions
Recommendation with Social Dimensions
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DOI：
10.1609/aaai.v30i1.9976
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Jiliang Tang;Suhang Wang;Xia Hu;Dawei Yin;Yingzhou Bi;Yi Chang;Huan Liu
中科院分区：
文献类型：
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作者：
Jiliang Tang;Suhang Wang;Xia Hu;Dawei Yin;Yingzhou Bi;Yi Chang;Huan Liu
社交媒体的普遍存在极大地丰富了在线用户的社交活动，产生了丰富的社会关系。社会关系为推荐提供了一个独立的来源，为推荐系统带来了新的机遇。利用社会关系来提高推荐性能近年来引起了人们的极大关注。大多数现有的社会推荐系统处理社会关系均匀，并利用直接连接（或强依赖连接）。然而，在线社交网络中的连接本质上是异质的，并且是各种关系的复合体。虽然在线社交网络中的连接用户形成群组，并且群组中的用户共享相似的兴趣，但是当这些用户不直接连接时，在这些用户之间建立弱依赖性连接。在本文中，我们研究如何利用社会关系的异质性和弱依赖连接的推荐。特别是，我们采用社会维度，同时捕捉异质性的社会关系和弱依赖连接，并提供原则性的方法来建模社会维度，并提出了一个建议框架SoDimRec，其中包括异质性的社会关系和弱依赖连接的基础上社会维度。在真实数据集上的实验结果证明了该框架的有效性。我们进行进一步的实验，以了解社会层面的重要作用，在拟议的框架。
The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations to improve recommendation performance attracts a great amount of attention in recent years. Most existing social recommender systems treat social relations homogeneously and make use of direct connections (or strong dependency connections). However, connections in online social networks are intrinsically heterogeneous and are a composite of various relations. While connected users in online social networks form groups, and users in a group share similar interests, weak dependency connections are established among these users when they are not directly connected. In this paper, we investigate how to exploit the heterogeneity of social relations and weak dependency connections for recommendation. In particular, we employ social dimensions to simultaneously capture heterogeneity of social relations and weak dependency connections, and provide principled ways to model social dimensions, and propose a recommendation framework SoDimRec which incorporates heterogeneity of social relations and weak dependency connections based on social dimensions. Experimental results on real-world data sets demonstrate the effectiveness of the proposed framework. We conduct further experiments to understand the important role of social dimensions in the proposed framework.