Recommender systems for product bundling

Recommender systems (RSs) enhance e-commerce sales by recommending relevant products to their customers. RSs aim at implementing the firm's web-based marketing strategy to increase revenues. Generating bundles is an example of a marketing strategy that aims to satisfy consumer needs and preferences, and at the same time, to increase customers' buying scope and the firm's income. Thus, finding and recommending an optimal and personal bundle becomes very important. In this paper we introduce a novel model of bundle recommendations that integrates collaborative filtering (CF) techniques, personalized demand functions, and price modeling. This model provides a recommendation list by finding pairs of products that maximizes both, the probability of their purchase by the user and the revenue received by selling this bundles.