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Research and Design of an Efficient Collaborative Filtering Predication Algorithm

机译:高效协同过滤预测算法的研究与设计

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摘要

Currently collaborative filtering has been widespread used to solve the problem of information overload. However there still remain two major limitations,data sparsity and scalability. In this paper, we explore a new collaborative filtering algorithm to solve the problem of data scalability and improve the predication accuracy. It uses a binary tree to store partitioned items. In the process of tree formation, a K-means clustering is used to partition data and create the neighbor of similar items, and then predication based on a smaller item database is performed.Since the preliminary clustering greatly reduces the search space, the search for similar neighbor items will be faster than for the entire database. In addition, the cluster that contains similar items is cohesive, thus it can produce a higher overall accuracy. The experimental results argue that our algorithm obviously outperforms current CF algorithms and it is feasible and efficient.
机译:当前,协作过滤已被广泛用于解决信息过载的问题。但是,仍然存在两个主要限制,数据稀疏性和可伸缩性。在本文中,我们探索了一种新的协作过滤算法,以解决数据可伸缩性问题并提高预测准确性。它使用二叉树来存储分区项。在树的形成过程中,使用K-means聚类对数据进行划分并创建相似项目的邻居,然后基于较小的项目数据库进行预测。由于初步聚类极大地减少了搜索空间,因此对类似的邻居项将比整个数据库快。此外,包含相似项目的群集具有凝聚力,因此可以产生更高的总体准确性。实验结果表明,我们的算法明显优于当前的CF算法,既可行又高效。

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