Graph-based bootstrapped latent recommendation model

IF 5.9 3区 管理学 Q1 BUSINESS
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引用次数: 0

Abstract

As an important means to optimize organizational profitability, recommendation systems have been widely applied on e-commerce platforms in recent years. Their goal is to identify products of interest from which users have not browsed. To achieve this, prior work often relies on negative sampling strategies to guide the learning of user and product representations. In these strategies, products that users have not browsed are treated as negative labeled samples (products that users dislike). However, the negative sampling strategy fundamentally contradicts the goal of recommendation systems. With the number of products further increases, more “positive but not been browsed” products will be treated as negative labeled samples, leading to the introduction of noisy supervision signals during model training and thereby affecting recommendation performance. This paper proposes a Graph-based Bootstrapped Latent Recommendation model, dubbed GBLR. GBLR is a self-supervised framework that is trained using only positive user–product pairs. It utilizes a graph convolutional network to aggregate local neighborhood features of users and products, bootstrapping latent contrastive views. Subsequently, a symmetric cosine similarity loss function aligns the contrastive views of positive user-product pairs, guiding the model to learn consistent representations of users and products. With this self-supervised approach, the model can effectively learn the user and product representations in the absence of negative labeled samples. Experiments on three public datasets show that the proposed GBLR can effectively complete the recommendation task and outperforms the state-of-the-art baseline models. In the era of e-commerce, the innovative research on recommendation methods conducted in this work can optimize platform operations, enhance user experience and merchant revenue, thereby achieving a win–win situation for all parties involved, and holds significant practical value.

基于图形的引导式潜在推荐模型
作为优化组织盈利能力的重要手段,推荐系统近年来被广泛应用于电子商务平台。其目标是识别用户未浏览过的感兴趣产品。为了实现这一目标,先前的工作通常依赖于负抽样策略来指导用户和产品表征的学习。在这些策略中,用户未浏览过的产品被视为负标签样本(用户不喜欢的产品)。然而,负抽样策略从根本上违背了推荐系统的目标。随着产品数量的进一步增加,更多 "积极但未浏览过 "的产品将被视为负标签样本,导致在模型训练过程中引入噪声监督信号,从而影响推荐性能。本文提出了一种基于图的引导式潜在推荐模型,称为 GBLR。GBLR 是一个自监督框架,只使用正用户-产品对进行训练。它利用图卷积网络聚合用户和产品的本地邻域特征,引导潜在对比观点。随后,对称余弦相似性损失函数将正向用户-产品配对的对比视图对齐,引导模型学习用户和产品的一致表征。通过这种自我监督的方法,该模型可以在没有负标签样本的情况下有效地学习用户和产品表征。在三个公共数据集上的实验表明,所提出的 GBLR 可以有效地完成推荐任务,并且优于最先进的基线模型。在电子商务时代,本文对推荐方法的创新研究可以优化平台运营、提升用户体验和商家收益,从而实现多方共赢,具有重要的实用价值。
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来源期刊
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications 工程技术-计算机:跨学科应用
CiteScore
10.10
自引率
8.30%
发文量
97
审稿时长
63 days
期刊介绍: Electronic Commerce Research and Applications aims to create and disseminate enduring knowledge for the fast-changing e-commerce environment. A major dilemma in e-commerce research is how to achieve a balance between the currency and the life span of knowledge. Electronic Commerce Research and Applications will contribute to the establishment of a research community to create the knowledge, technology, theory, and applications for the development of electronic commerce. This is targeted at the intersection of technological potential and business aims.
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