Recommendation Systems with Purchase Data

Abstract

Many firms use decision tools called “automatic recommendation systems” that attempt to analyze a customer's purchase history and identify products the customer may buy if the firm were to bring these products to the customer's attention. Much of the research in the literature today attempts to recommend products that have a high probability of purchase (conditional on the customer's history). However, the author posits that the recommendation decision should be based not on purchase probabilities but rather on the sensitivity of purchase probabilities to the recommendation action. This article attempts to model carefully the role of firms' recommendation actions in modifying customers' buying behaviors relative to what the customers would do without such a recommendation intervention. The author proposes a simple consumer behavior model that accommodates a transparent role for a firm's recommendation actions. The model is expressed in econometric terms so that it can be estimated with available data. The author studies these ideas using purchase data from a real e-commerce firm and compares the performance of the proposed main model with the performance of benchmark models. The author shows that the main model is better than benchmark models on key measures.