Personalization in E-Grocery: Top-N versus Top-k Rankings

TL;DR

This paper demonstrates that personalized long rankings (top-N) in e-grocery recommender systems significantly reduce information overload, improve user experience, and increase revenue compared to traditional short rankings (top-k).

Contribution

It introduces a novel top-N personalized ranking approach for e-grocery recommender systems and empirically validates its benefits over top-k rankings.

Findings

Reduces information overload by 29.4%

Lowers user search time by 3.3 seconds per item

Increases revenue by 7%

Abstract

Business success in e-commerce depends on customer perceived value. A customer with high perceived value buys, returns, and recommends items. The perceived value is at risk whenever the information load harms users' shopping experience. In e-grocery, shoppers face an overwhelming number of items, the majority of which is irrelevant for the shopper. Recommender systems (RS) enable businesses to master information overload (IO) by providing users with an item ranking by relevance. Prior work proposes RS with short personalized rankings (top-k). Given large order sizes and high user heterogeneity in e-grocery, top-k RS are insufficient to diminish IO in this domain. To fill this gap and raise business performance, this paper introduces an RS with a personalized long ranking (top-N). Undertaking a randomized field experiment, the paper establishes the merit of shifting from top-k to top-N rankings. Specifically, the proposed RS reduces IO by 29.4% and lowers users' search time by 3.3 seconds per item. The field experiment also reveals a 7% uplift in revenue due to the top-N ranking. Substantial benefits for the customer and the company highlight the business value of top-N rankings as a new design requirement for recommender systems in e-grocery.