当求变遇到意外:将求变行为融入意外推荐系统的设计

IF 5 3区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Pan Li, Alexander Tuzhilin
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引用次数: 0

摘要

本文研究了推荐系统应用中消费者的品种寻求行为,并基于过去的消费记录提出了一个综合的框架来衡量这种行为。通过在阿里巴巴进行的用户问卷研究验证了所提出框架的有效性,我们构建的品种寻找措施与消费者自我报告的品种寻找行为水平非常吻合。随后,我们提出了一个推荐框架,该框架将数据挖掘文献中确定的品种寻求水平与意想不到的推荐系统相结合,以解决消费者对产品多样性的异质欲望,在该框架中,我们向寻求品种的消费者提供更多意想不到的产品推荐,反之亦然。通过对三种不同推荐场景的离线实验和在主要视频流平台上的大规模在线控制实验,我们证明了遵循我们推荐框架的那些模型显着提高了各种业务绩效指标,并为公司产生了切实的经济影响。我们的研究结果对更好地理解消费者的品种寻求行为和设计推荐系统具有重要的管理意义。因此,在我们提出的框架中,性能最好的模型由公司部署,以服务视频流平台上的所有消费者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems
In this paper, we study the consumers’ variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers’ self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers’ heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers’ variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform.
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来源期刊
CiteScore
9.10
自引率
8.20%
发文量
120
期刊介绍: ISR (Information Systems Research) is a journal of INFORMS, the Institute for Operations Research and the Management Sciences. Information Systems Research is a leading international journal of theory, research, and intellectual development, focused on information systems in organizations, institutions, the economy, and society.
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