Electronic Commerce Research and Applications最新文献

Grasping complementary relationships between fashion product pairings is gaining increasing attention in the e-commerce field. Current methods primarily utilize visual cues to assess compatibility, which, despite their efficacy, often lack sufficient explainability. Meanwhile, the rich semantic details embedded in product attributes remain largely unexplored. To tackle this, we propose a novel framework called Explainable Attribute-augmented Neural framework (EAN), which integrates comprehensive attribute and visual data, enabling explainability in fashion product compatibility modeling. We conduct quantitative and qualitative experiments to demonstrate the effectiveness and explainability of our proposed framework. The practical significance of our research is twofold. Firstly, it helps consumers understand the underlying reasons for fashion item pairings, thereby assisting them in refining their dressing combinations. Secondly, it provides novel perspectives for product design and assists e-commerce platforms in creating more effective product marketing combinations.

Online question-and-answer communities are seriously threatened by low user participation. There is currently a rare comprehensive study on the knowledge contribution pattern of consistently active participants and the moderating role of peer recognition, which can help improve low participation and reengage inactive users, despite researchers having examined the various facets of knowledge contribution and made helpful suggestions. As per the self-determination and social cognitive theory, the communal environment impacts peers and imitates role models or reliable sources in their involvement patterns. We have examined StackOverflow’s most reliable active users from 2010 to 2020 using the social cognition and self-determination theories to use the findings to reactivate dormant users. We have used a two-step dynamic system GMM model to get robust and reliable findings. The research discovered that peer repudiation, reputation, and online social interactions favorably affect the contributed knowledge. However, knowledge-seeking and earning virtual badges such as gold and bronze usually negatively impact it. Furthermore, it was revealed that the effect of virtual badges on contributed knowledge was positively moderated by peer recognition. However, peer recognition reduces the benefits of social interaction and reputation on the contributed knowledge. The study’s findings advance the body of knowledge and provide thorough management implications for raising low participation, reengaging inactive users, and cultivating a culture of innovative sharing of knowledge.

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.