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Trust-based collaborative filtering algorithm in social network

机译:社交网络中基于信任的协同过滤算法

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摘要

In order to improve the accuracy of recommendation algorithm in social network applications, a new recommendation method based on traditional collaborative filtering recommendation algorithm, which called Trust-based Collaborative Filtering, is proposed and verified in this paper. Firstly, we analyze users' behaviors and relationships in social network, and propose a trust calculation method based on Dijkstra's algorithm. Secondly, we integrate users' trust information into the collaborative filtering algorithm to recommend in social network. Finally, we choose Flixster dataset to validate the proposed model and use the Mean Absolute Error (MAE) as the evaluation metric. Experiment results show that Trust-based CF significantly improves the recommendation quality in social network.
机译:为了提高推荐算法在社交网络应用中的准确性,提出并验证了一种基于传统协同过滤推荐算法的推荐方法,即基于信任的协同过滤。首先,我们分析了用户在社交网络中的行为和关系,并提出了一种基于Dijkstra算法的信任度计算方法。其次,我们将用户的信任信息整合到协同过滤算法中,以在社交网络中进行推荐。最后,我们选择Flixster数据集来验证提出的模型,并使用平均绝对误差(MAE)作为评估指标。实验结果表明,基于信任的CF显着提高了社交网络的推荐质量。

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