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An Improved Collaborative Filtering Algorithm Based on User Interest

机译：一种基于用户兴趣的改进的协同过滤算法

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摘要

With the development of personalized services, collaborative filtering techniques have been successfully applied to the network recommendation system. But sparse data seriously affect the performance of collaborative filtering algorithms. To alleviate the impact of data sparseness, using user interest information, an improved user-based clustering Collaborative Filtering (CF) algorithm is proposed in this paper, which improves the algorithm by two ways: user similarity calculating method and user-item rating matrix extended. The experimental results show that the algorithm could describe the user similarity more accurately and alleviate the impact of data sparseness in collaborative filtering algorithm. Also the results show that it can improve the accuracy of the collaborative recommendation algorithm.
机译：随着个性化服务的发展，协作过滤技术已成功应用于网络推荐系统。但是稀疏数据会严重影响协作过滤算法的性能。为了减轻数据稀疏性的影响，本文提出了一种基于用户兴趣信息的改进的基于用户的聚类协同过滤算法，该算法通过用户相似度计算方法和用户项评价矩阵扩展两种方式对算法进行了改进。 。实验结果表明，该算法可以更准确地描述用户相似度，减轻了数据稀疏度对协同过滤算法的影响。结果还表明，该算法可以提高协同推荐算法的准确性。

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