Recommender systems for product bundling

摘要

Recommender systems (RS) are a class of information filter applications whose main goal is to provide personalized recommendations, content, and services to users. Recommendation services may support a firm's marketing strategy and contribute to increase revenues. Most RS methods were designed to provide recommendations of single items. Generating bundle recommendations, i.e., recommendations of two or more items together, can satisfy consumer needs, while at the same time increase customers’ buying scope and the firm's income. Thus, finding and recommending an optimal and personal bundle becomes very important. Recommendation of bundles of products should also involve personalized pricing to predict which price should be offered to a user in order for the bundle to maximize purchase probability. However, most recommendation methods do not involve such personal price adjustment.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.