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Collaborative filtering using orthogonal nonnegative matrix tri-factorization

机译：使用正交非负矩阵三因子分解的协同过滤

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摘要

Collaborative filtering aims at predicting a test user's ratings for new items by integrating other like-minded users' rating information. The key assumption is that users sharing the same ratings on past items tend to agree on new items. Traditional collaborative filtering methods can mainly be divided into two classes: memory-based and model-based. The memory-based approaches generally suffer from two fundamental problems: sparsity and scalability, and the model-based approaches usually cost too much on establishing a model and have many parameters to be tuned.rnIn this paper, we propose a novel framework for collaborative filtering by applying orthogonal nonnegative matrix tri-factorization (ONMTF), which (1) alleviates the sparsity problem via matrix factorization; (2) solves the scalability problem by simultaneously clustering rows and columns of the user-item matrix. Experiments on the benchmark data set show that our algorithm is indeed more tolerant against both sparsity and scalability, and achieves good performance in the mean time.
机译：协作过滤旨在通过集成其他志趣相投的用户的评分信息来预测测试用户对新项目的评分。关键的假设是，对过去的商品具有相同评分的用户往往会同意新的商品。传统的协同过滤方法主要可以分为两类：基于内存的和基于模型的。基于内存的方法通常会遇到两个基本问题：稀疏性和可伸缩性，而基于模型的方法通常在建立模型上花费太多，并且需要调整许多参数。在本文中，我们提出了一种新颖的协作过滤框架通过应用正交非负矩阵三因子分解（ONMTF），（1）通过矩阵因子分解缓解稀疏性问题； （2）通过同时对用户项矩阵的行和列进行聚类来解决可伸缩性问题。在基准数据集上进行的实验表明，我们的算法确实对稀疏性和可伸缩性具有更高的容忍度，并在同时达到了良好的性能。

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  • 来源
    《Information Processing & Management》 |2009年第3期|368-379|共12页
  • 作者

    Gang Chen; Fei Wang; Changshui Zhang;

  • 作者单位

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;

    State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    collaborative filtering; orthogonal nonnegative matrix trifactorization; co-clustering; fusion;

    机译：协同过滤正交非负矩阵三因子分解;共同集群融合;

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