Group Buying Recommendation Model Based on Multi-task Learning

TL;DR

This paper introduces MGBR, a multi-task learning-based recommendation model tailored for group buying, addressing the unique challenges of group buying recommendations with improved performance over existing models.

Contribution

The paper proposes a novel multi-task learning framework for group buying recommendation, incorporating collaborative expert networks and auxiliary losses to enhance recommendation accuracy.

Findings

MGBR outperforms previous recommendation models in experiments.

Designed components significantly improve model performance.

Auxiliary losses refine representation learning.

Abstract

In recent years, group buying has become one popular kind of online shopping activity, thanks to its larger sales and lower unit price. Unfortunately, research seldom focuses on recommendations specifically for group buying by now. Although some recommendation models have been proposed for group recommendation, they can not be directly used to achieve real-world group buying recommendation, due to the essential difference between group recommendation and group buying recommendation. In this paper, we first formalize the task of group buying recommendations into two sub-tasks. Then, based on our insights into the correlations and interactions between the two sub-tasks, we propose a novel recommendation model for group buying, MGBR, built mainly with a multi-task learning module. To improve recommendation performance further, we devise some collaborative expert networks and adjusted gates in the multi-task learning module, to promote the information interaction between the two sub-tasks. Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model. Our extensive experiments not only demonstrate that the augmented representations in our model result in better performance than previous recommendation models, but also justify the impacts of the specially designed components in our model.