Online grocery shopping recommender systems: Common approaches and practices

Abstract

Food recommender systems have been developed for the online environment to support shoppers in making informed decisions. These systems analyze the extensive data collected to infer consumer preferences and needs, providing relevant product recommendations accordingly. Despite the potential of recommender systems as a strategic marketing tool in the online grocery shopping environment, there has been limited effort to systematically analyze approaches of prior studies on recommender systems for online grocery shoppers along the five stages of recommendation delivery: (1) identify recommendation goal, (2) acquire consumer data, (3) compute, (4) evaluate, and (5) present the recommendation. Therefore, this paper examines the advancements in each stage of delivering grocery recommendations to consumers from 2018 to March 2023. We performed a search strategy resulting in 50 papers dedicated to recommender systems for online grocery shoppers, which contrasts with previous research that typically examined recipe and meal recommendations that were merely meant to inspire users on what to cook. Findings reveal a prevalence of preference-based systems with limited integration of explicit consumer data, and often lacking consent for implicit data usage. While advanced deep neural network models are getting more attention in the literature, evaluation methods tend to be system-oriented, overlooking essential user feedback and the efficacy of general metrics. This systematic literature review underscores the necessity for consumer engagement in system and interface design, aiming for grocery recommendation systems that improve customer experience, by ensuring inclusivity and prioritizing user-centered design.