Events

Mr. Pan Li
Ph.D. Candidate in Information System
Stern School of Business
New York University

ABSTRACT

Variety seekers represent those customers who might get bored of the products they purchased before, and therefore prefer new and fresh content to expand their horizons. Despite its prevalence, variety-seeking behavior has hardly been studied in recommendation applications, due to multiple limitations in existing variety-seeking methods. To fill the research gap, we present a variety-seeking framework in this paper to measure the level of variety-seeking behaviors of each customer 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, utilizing our proposed variety-seeking framework, we present a recommendation framework that combines the variety-seeking levels with unexpected recommender systems in the data mining literature, to address consumers’ heterogenous desire for product variety. Specifically, we will provide more unexpected product recommendations to variety-seeking consumers, and less unexpected products to consistency-seeking consumers. Through offline experiments on 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 would 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 accordingly. 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.