Scalable Collaborative Ranking for Personalized Prediction
Scalable Collaborative Ranking for Personalized Prediction
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DOI：
10.1080/01621459.2019.1691562
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发表时间：
2020-01
影响因子：
3.7
通讯作者：
Ben Dai;Xiaotong Shen;Junhui Wang;A. Qu
中科院分区：
文献类型：
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作者：
Ben Dai;Xiaotong Shen;Junhui Wang;A. Qu
摘要个性化预测是一项重要而又具有挑战性的任务，它在给定有限信息的情况下预测用户对大量项目的特定偏好。它通常被建模为某些专注于顺序或连续评级的推荐系统，如协作过滤和基于内容的过滤。在本文中，我们提出了一种新的协作排名系统，以预测每个用户在给定搜索查询时最喜欢的项目。具体地说，我们提出了一种基于排名函数的ψ排名器，该排名函数通过潜在因素模型融合了关于用户、项目和搜索查询的信息。此外，我们还证明了所提出的非凸代理成对ψ-Lost在四种常见的二部排序损失下表现良好，例如总和损失、成对零-1损失、折扣累积收益和平均平均精度。我们提出了一种并行计算策略，通过凸规划的差异和分块逐次上界最小化来优化两级非凸组分的难处理损失。理论上，我们建立了ψ-排序器的一个概率误差界，并证明了在二部排序的一般框架下，即使模型参数的维度随样本大小而发散，它的排序误差也具有很强的收敛速度。因此，这一结果也表明，ψ-排名器在两部分排名中的表现优于两种主要方法：成对排名和评分。最后，我们通过与文献中一些强有力的竞争对手的比较，通过模拟例子和Expedia预订数据，证明了ψ-Runker的实用性。这篇文章的补充材料可以在网上找到。
Abstract Personalized prediction presents an important yet challenging task, which predicts user-specific preferences on a large number of items given limited information. It is often modeled as certain recommender systems focusing on ordinal or continuous ratings, as in collaborative filtering and content-based filtering. In this article, we propose a new collaborative ranking system to predict most-preferred items for each user given search queries. Particularly, we propose a ψ-ranker based on ranking functions incorporating information on users, items, and search queries through latent factor models. Moreover, we show that the proposed nonconvex surrogate pairwise ψ-loss performs well under four popular bipartite ranking losses, such as the sum loss, pairwise zero-one loss, discounted cumulative gain, and mean average precision. We develop a parallel computing strategy to optimize the intractable loss of two levels of nonconvex components through difference of convex programming and block successive upper-bound minimization. Theoretically, we establish a probabilistic error bound for the ψ-ranker and show that its ranking error has a sharp rate of convergence in the general framework of bipartite ranking, even when the dimension of the model parameters diverges with the sample size. Consequently, this result also indicates that the ψ-ranker performs better than two major approaches in bipartite ranking: pairwise ranking and scoring. Finally, we demonstrate the utility of the ψ-ranker by comparing it with some strong competitors in the literature through simulated examples as well as Expedia booking data. Supplementary materials for this article are available online.