Bayesian-inference based recommendation in online social networks
Bayesian-inference based recommendation in online social networks
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DOI：
10.1109/infcom.2011.5935224
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发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
Xiwang Yang;Yang Guo;Yong Liu
中科院分区：
文献类型：
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作者：
Xiwang Yang;Yang Guo;Yong Liu
在本文中，我们提出了一个基于贝叶斯推理的在线社交网络推荐系统。在我们的系统中，用户与朋友分享他们的电影评级。一对朋友之间的评级相似性是通过一组条件概率来衡量的，这些条件概率来自他们的相互评级历史。用户沿着社交网络向他的直接和间接朋友传播电影评级查询。基于查询响应，构造贝叶斯网络来推断查询用户的评级。我们开发的分布式协议，可以很容易地实现在线社交网络。所提出的算法进行评估，在一个合成的社会网络来自电影评级数据集的真实的用户。我们表明，贝叶斯推理为基础的推荐提供个性化的建议，准确的传统CF方法，并允许推荐质量和推荐数量之间的灵活权衡。
In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.