Social Recommendation With Evolutionary Opinion Dynamics
Social Recommendation With Evolutionary Opinion Dynamics
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DOI：
10.1109/tsmc.2018.2854000
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Fei Xiong;Ximeng Wang;Shirui Pan;Hong Yang;Haishuai Wang;Chengqi Zhang
中科院分区：
文献类型：
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作者：
Fei Xiong;Ximeng Wang;Shirui Pan;Hong Yang;Haishuai Wang;Chengqi Zhang
当在线社交网络中的用户做出决定时，他们往往会受到邻居的影响。社会推荐模型利用社会信息来揭示邻居对用户偏好的影响，这种影响通常通过邻居偏好的线性叠加或全局信任传播来描述。进一步的探索需要进行，以确定其他用户的在线互动行为的影响模式是否得到充分的描述。在本文中，我们介绍了统计物理领域的进化意见动力学到推荐系统，其他用户的影响的特点。我们提出了一个意见动态模型的进化博弈论。为了描述在线用户交互，我们定义了两个用户之间的交互过程中的策略，并提出了每个策略的估计收视率的误差方面的回报。因此，用户行为与他们的偏好和评级相关联。此外，我们根据用户在社交网络中的拓扑角色来衡量用户影响力。我们将进化的意见动态和用户的影响力到推荐框架的预测未知的评级。在两个真实数据集上的实验结果表明，我们的方法在准确性方面优于最先进的模型，并且对于冷启动用户也表现良好。我们的方法减少了分歧的用户偏好，根据在线意见的互动。此外，我们的方法具有近似的计算复杂度与矩阵分解，并导致更少的计算比国家的最先进的模型。我们的方法是相当普遍的，并表明，在社会物理学，统计学和其他研究领域的研究可能会涉及到推荐，以提高性能。
When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.