Learning binary codes for collaborative filtering
Learning binary codes for collaborative filtering
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DOI：
10.1145/2339530.2339611
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Ke Zhou;H. Zha
中科院分区：
文献类型：
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作者：
Ke Zhou;H. Zha
本文解决了在大用户和项目空间的情况下提出推荐的效率问题。特别是，我们解决了学习协作过滤的二进制代码的问题，这使我们能够以独立于项目总数的时间复杂度有效地提出建议。我们建议为用户和项目构建二进制代码，以便可以通过各自二进制代码之间的汉明距离准确地保留用户对项目的偏好。通过使用两个损失函数来测量训练和预测评分之间的差异程度，我们将学习二进制代码的问题表述为离散优化问题。尽管这个优化问题一般来说很棘手，但我们开发了有效的松弛方法，可以通过现有方法有效解决。此外，我们研究了两种从松弛解中获取二进制代码的方法。对三个公共领域数据集进行了评估，结果表明我们提出的方法优于几种基准替代方法。
This paper tackles the efficiency problem of making recommendations in the context of large user and item spaces. In particular, we address the problem of learning binary codes for collaborative filtering, which enables us to efficiently make recommendations with time complexity that is independent of the total number of items. We propose to construct binary codes for users and items such that the preference of users over items can be accurately preserved by the Hamming distance between their respective binary codes. By using two loss functions measuring the degree of divergence between the training and predicted ratings, we formulate the problem of learning binary codes as a discrete optimization problem. Although this optimization problem is intractable in general, we develop effective relaxations that can be efficiently solved by existing methods. Moreover, we investigate two methods to obtain the binary codes from the relaxed solutions. Evaluations are conducted on three public-domain data sets and the results suggest that our proposed method outperforms several baseline alternatives.