This paper introduces a novel model of bundle recommendations that integrates collaborative filtering (CF) techniques, demand functions, and price modeling. This model maximizes the expected revenue of a recommendation list by finding pairs of products and pricing them in a way that maximizes both the probability of its purchase by the user and the revenue received by selling the bundle.
Experiments with several real-world datasets have been conducted in order to evaluate the accuracy of the bundling model predictions. This paper compares the proposed method with several state-of-the-art methods (collaborative filtering and SVD). It has been found that using bundle recommendation can improve the accuracy of results. Furthermore, the suggested price recommendation model provides a good estimate of the actual price paid by the user and at the same time can increase the firm's income.