Scalable Collaborative Ranking for Personalized Prediction

• 成果类型: Article
• 署名作者: Dai, Ben; Shen, Xiaotong; Wang, Junhui; Qu, Annie
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; City University of Hong Kong; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1691562
• 发表日期: 2021
• 页码: 1215-1223
• 关键词: minimization; CONVERGENCE; systems

摘要

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 psi-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 psi-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 psi-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 psi-ranker performs better than two major approaches in bipartite ranking: pairwise ranking and scoring. Finally, we demonstrate the utility of the psi-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.