Mining Large-Scale Retail Data

Understanding customer buying patterns is of great interest in the retail industry. Applications include targeted advertising, optimized product placement, and cross-promotions. Association rules, expressed as A → B (if A then B) are a common and easily understandable ways to represent buying patterns. While the problem of mining such rules has received considerable attention over the past years, most of the approaches proposed have only be evaluated on relatively small datasets, and struggle at large scale. In the context of the Datalyse project 1, Intermarché, our industrial partner, has given us access to 2 years of sales data: 3.5B sales records, 300M tickets, 9M customers, 200k products. This constitutes an opportunity to re-visit the problem association rules mining in the context of “big data”. In the remainder of this paper, I will first give an overview of our work on designing mining algorithms adapted to long-tailed datasets. Then, I will describe our evaluation of quality measures for ranking association rules. Finally, I will present the systems architecture deployed to apply mining in production at Intermarché.