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A Trust-Based Prediction Approach for Recommendation System

机译:一种基于信任的推荐系统预测方法

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The recommendation system has been widely used in e-commerce, but still suffers from data sparsity and cold-start problems. This paper combines the user trust relationship with the collaborative filtering recommendation system and puts forward the recommendation approach based on trust delivery (TDR), in order to solve the above two problems. Through calculating the quantifying trust values between users, the prediction score of an unrated item can be figured out to achieve effective recommendation. Compared with other recommendation algorithms, TDR achieves better performance on standard Mean Absolute Error (MAE) and Coverage.
机译:推荐系统已经在电子商务中广泛使用,但是仍然遭受数据稀疏和冷启动问题的困扰。本文将用户信任关系与协同过滤推荐系统相结合,提出了基于信任传递的推荐方法,以解决上述两个问题。通过计算用户之间的量化信任值,可以算出未评级项目的预测得分,以实现有效推荐。与其他推荐算法相比,TDR在标准平均绝对误差(MAE)和覆盖率方面实现了更好的性能。

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