Walmart Online Grocery Personalization: Behavioral Insights and Basket Recommendations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9975)


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.


Recommender System Transaction Data Recommendation Algorithm Grocery Shopping Shopping Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.@WalmartLabsSan BrunoUSA

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