When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems

• 成果类型: Article
• 署名作者: Li, Pan; Tuzhilin, Alexander
• 署名单位: University System of Georgia; Georgia Institute of Technology; New York University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.0053
• 发表日期: 2024
• 页码: 1257-1273
• 关键词: optimum stimulation level; consumer; IMPACT; MODEL; time; consumption; inertia; context; demand; search

摘要

Variety seekers are those customers who easily get bored with the products they purchased before and, therefore, prefer new and fresh content to expand their horizons. Despite its prevalence, variety-seeking behavior is hardly studied in recommendation applications because of various limitations in existing variety-seeking measures. To fill the research gap, we present a variety-seeking framework in this paper to measure the level of variety-seeking behavior of customers in recommendations based on their consumption records. We validate the effectiveness of our framework through user questionnaire studies conducted at Alibaba, where our variety-seeking measures match well with consumers' self-reported levels of their variety-seeking behaviors. Furthermore, we 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 has been deployed by the company to serve all consumers on the video-streaming platform.