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首页> 外文会议>Asia-Pacific Web Conference(APWeb 2006); 20060116-18; Harbin(CN) >Optimizing Collaborative Filtering by Interpolating the Individual and Group Behaviors
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Optimizing Collaborative Filtering by Interpolating the Individual and Group Behaviors

机译：通过内插个人和群体行为来优化协作过滤

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摘要

Collaborative filtering has been very successful in both research and E-commence applications. One of the most popular collaborative filtering algorithms is the k-Nearest Neighbor (KNN) method, which finds k nearest neighbors for a given user to predict his interests. Previous research on KNN algorithm usually suffers from the data sparseness problem, because the quantity of items users voted is really small. The problem is more severe in web-based applications. Cluster-based collaborative filtering has been proposed to solve the sparseness problem by averaging the opinions of the similar users. However, it does not bring consistent improvement on the performance of collaborative filtering since it produces less-personal prediction. In this paper, we propose a clustering-based KNN method, which combines the iterative clustering algorithm and the KNN to improve the performance of collaborative filtering. Using the iterative clustering approach, the sparseness problem could be solved by fully exploiting the voting information first. Then, as a smoothing method to the KNN method, cluster-based KNN is used to optimize the performance of collaborative filtering. The experimental results show that our proposed cluster-based KNN method can perform consistently better than the traditional KNN method and clustering-based method in large-scale data sets.
机译：协作过滤在研究和电子商务应用中都非常成功。最受欢迎的协作过滤算法之一是k最近邻居（KNN）方法，该方法为给定用户找到k个最近邻居以预测其兴趣。以前对KNN算法的研究通常会遇到数据稀疏问题，因为用户投票的项目数量确实很小。在基于Web的应用程序中，此问题更为严重。为了解决稀疏问题，提出了一种基于聚类的协同过滤算法，该算法通过平均相似用户的意见来解决。但是，由于它会产生较少的个人预测，因此无法带来协同过滤性能的持续改进。在本文中，我们提出了一种基于聚类的KNN方法，该方法将迭代聚类算法和KNN相结合，以提高协作过滤的性能。使用迭代聚类方法，可以通过首先充分利用投票信息来解决稀疏性问题。然后，作为对KNN方法的一种平滑方法，基于聚类的KNN用于优化协作过滤的性能。实验结果表明，在大规模数据集中，我们提出的基于聚类的KNN方法能够比传统的KNN和基于聚类的方法表现更好。

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