SQL-Rank: A Listwise Approach to Collaborative Ranking
SQL-Rank: A Listwise Approach to Collaborative Ranking
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发表时间：
2018-02
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通讯作者：
Liwei Wu;Cho-Jui Hsieh;J. Sharpnack
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作者：
Liwei Wu;Cho-Jui Hsieh;J. Sharpnack
在这篇文章中，我们提出了一种列表的方法来在推荐系统中以协作的方式构建特定于用户的排名。我们将列表方法与以前的逐点和成对方法进行了对比，后者分别基于将每个评级或每个成对比较视为独立的实例。通过扩展(曹等人)的工作。2007)，我们将列表协作排名作为排列模型下的最大似然，该模型基于低排名潜在分数矩阵将概率质量应用于排列。我们提出了一种新的算法--SQL-Rank，该算法能够适应连接和缺失数据，并且能够在线性时间内运行。基于排列模型的一种新的表示理论，我们提出了一个分析列表排序方法的理论框架。将该框架应用到协作排序中，我们得到了随着用户数和项目数的共同增长的渐近统计率。我们的结论是，我们的SQL-Rank方法通常优于当前最先进的隐式反馈算法，如加权MF和BPR，并且与显式反馈算法，如矩阵因式分解和协作排名相比，获得了良好的结果。
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instance respectively. By extending the work of (Cao et al. 2007), we cast listwise collaborative ranking as maximum likelihood under a permutation model which applies probability mass to permutations based on a low rank latent score matrix. We present a novel algorithm called SQL-Rank, which can accommodate ties and missing data and can run in linear time. We develop a theoretical framework for analyzing listwise ranking methods based on a novel representation theory for the permutation model. Applying this framework to collaborative ranking, we derive asymptotic statistical rates as the number of users and items grow together. We conclude by demonstrating that our SQL-Rank method often outperforms current state-of-the-art algorithms for implicit feedback such as Weighted-MF and BPR and achieve favorable results when compared to explicit feedback algorithms such as matrix factorization and collaborative ranking.