Abstract
Food is so personal. Each individual has her own shopping characteristics. In this paper, we introduce personalization for Walmart online grocery. Our contribution is twofold. First, we study shopping behaviors of Walmart online grocery customers. In contrast to traditional online shopping, grocery shopping demonstrates more repeated and frequent purchases with large orders. Secondly, we present a multi-level basket recommendation system. In this system, unlike typical recommender systems which usually concentrate on single item or bundle recommendations, we analyze a customer’s shopping basket holistically to understand her shopping tasks. We then use multi-level cobought models to recommend items for each of the purposes. At the stage of selecting particular items, we incorporate both the customers’ general and subtle preferences into decisions. We finally recommend the customer a series of items at checkout. Offline experiments show our system can reach 11 % item hit rate, 40 % subcategory hit rate and 70 % category hit rate. Online tests show it can reach more than 25 % order hit rate.
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Yuan, M., Pavlidis, Y., Jain, M., Caster, K. (2016). Walmart Online Grocery Personalization: Behavioral Insights and Basket Recommendations. In: Link, S., Trujillo, J. (eds) Advances in Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9975. Springer, Cham. https://doi.org/10.1007/978-3-319-47717-6_5
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DOI: https://doi.org/10.1007/978-3-319-47717-6_5
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