构建完整的协同过滤方法系统

Li Yu, Xiaoping Yang
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引用次数: 0

摘要

协同过滤是推荐系统中的一项关键技术。近年来,我们在以往的工作中发现了协同过滤中存在的一般邻域问题,在多社区或多利益的情况下可能导致致命的错误。为了克服这一问题,提出了基于社区的协同过滤(CFC)。不幸的是,CFC存在严重的稀疏性,这可能导致更差的性能。提出了各种改进方法来增强它。基于上述一系列方法,构建了一个完整的分层协同过滤方法系统(CFMS)。CFMS扩展了协同过滤,适应各种不同的情况。通过实验对各种CFMS方法进行了实证评价和比较。
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Building complete Collaborative Filtering Method System
Collaborative filtering (CF) is a key technique in recommender system. Recently, general neighborhood problem existing in collaborative filtering is identified in our previous work, which could result into fatal wrong under multi-community or multi-interest case. In order to overcome it, collaborative filtering based on community (CFC) is presented. Unfortunately, CFC suffers from severer sparsity, which could result into worse performance. Various improved methods are proposed to enhance it. Based on a series of above methods, a complete and hierarchical Collaborative Filtering Method System (CFMS) is build. CFMS extend collaborative filtering, adapting to various different cases. Experiments are made to empirically valuate and compare various methods of CFMS.
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