Bundle Recommendation for Budget-Conscious Consumers

Traditional recommender systems are dedicated to recommending items of interest to consumers, but consumers are more concerned about how to purchase optimal bundles (combinations of items) under the constraints of their limited budgets. In this paper, an approach for recommending bundles according to users’ budgets is proposed based on Graph Convolutional Networks (GCN). The GCN propagates over a heterogeneous graph consisting of the user-bundle interactions, user-item interactions, and bundle-item affiliations. The price attributes of bundles and items are also considered to capture users’ preferences at two levels: the price of a bundle as a whole and the price of individual items contained in the bundle. Besides Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG), a metric that takes into account the similarity between a recommended bundle and the user’s target bundle is designed to evaluate the performance of our model. Experimental results show that the model achieves state-of-the-art performance on three datasets. The approach of recommending bundles according to users’ budgets is more in line with the theory of consumer choice, which provides a fresh perspective for the research on recommender systems.