Open Access

ARTICLE

MAMGBR: Group-Buying Recommendation Model Based on Multi-Head Attention Mechanism and Multi-Task Learning

Zongzhe Xu, Ming Yu^*

College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China

* Corresponding Author: Ming Yu. Email:

Computers, Materials & Continua 2025, 84(2), 2805-2826. https://doi.org/10.32604/cmc.2025.066244

Received 02 April 2025; Accepted 07 May 2025; Issue published 03 July 2025

Abstract

As the group-buying model shows significant progress in attracting new users, enhancing user engagement, and increasing platform profitability, providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems. This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning, termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation (MAMGBR) model, specifically designed to optimize group-buying recommendations on e-commerce platforms. The core dataset of this study comes from the Chinese maternal and infant e-commerce platform “Beibei,” encompassing approximately 430,000 successful group-buying actions and over 120,000 users. The model focuses on two main tasks: recommending items for group organizers (Task I) and recommending participants for a given group-buying event (Task II). In model evaluation, MAMGBR achieves an MRR@10 of 0.7696 for Task I, marking a 20.23% improvement over baseline models. Furthermore, in Task II, where complex interaction patterns prevail, MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users, items, and participants, leading to a 24.08% increase in MRR@100 under a 1:99 sample ratio. Experimental results show that compared to benchmark models, such as NGCF and EATNN, MAMGBR’s integration of multi-head attention mechanisms, expert networks, and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios, significantly enhancing recommendation accuracy and platform group-buying success rates.

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Keywords

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