Frontiers: Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

Shuang Zheng, Siliang Tong, Hyeokkoo Eric Kwon, Gordon Burtch, Xianneng Li

Abstract: This study examines the impact of a query recommender system on user search behavior, sales volume, and consumption diversity within a leading mobile food delivery app in Asia. We find that access to a query recommender increases consumer purchase volumes by 1%–2% over 30 days while broadening consumption diversity at both the individual and market levels. Exploring the mechanisms by which these effects arise, we highlight the complementary, balancing role of query auto-completion features. Whereas the query recommender helps to expand a user’s consideration set by suggesting alternative and adjacent queries, the auto-complete feature helps to extend and refine the queries in a personalized manner. Our findings highlight the potential of query recommenders for increasing demand while enhancing consumer exploration and consumption diversity, particularly when deployed in tandem with auto-complete. Our study contributes to the literature on search behavior and recommendation systems, offering actionable insights for platform managers into the strategic design and integration of query recommenders to improve user engagement and market outcomes. History: Catherine Tucker served as the senior editor. Funding: This research is supported by National Natural Science Foundation of China (NSFC) [Grants 72071029 and 72231010]; the National Research Foundation, Singapore, under its AI Singapore Programme (AISG) [Grant AISG3-GV-2021-006], and the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 [Grant RG44/24]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mksc.2024.1121.