Electronic Commerce Research最新文献

Digital technologies such as artificial intelligence (AI) are driving the growth of live-streaming e-commerce. As a result, a rising number of virtual anchors who are appearing in live-streaming e-commerce, generating customer engagement. However, whether the virtual anchor driven by different types of AI–human collaboration has different impacts on consumer engagement needs to be further investigated. By adopting the use and gratifications theory, this paper investigated the mechanism of the virtual anchor driven by AI–human collaboration on consumer engagement and the moderating effect of the humorous response. The results of two studies demonstrated that the virtual anchor driven by assisted AI–human collaboration contributed to higher levels of perceived playfulness than those driven by supervised AI–human collaboration, leading to increased customer engagement. Meanwhile, it was found that the differences between the supervised and assisted virtual anchor driven by AI–human collaboration on perceived playfulness decrease when the humorous response is present. This paper fills in the gap in virtual anchor research by providing insights into how to enhance the positive effect of customer engagement and giving suggestions for future research on virtual anchors.

This paper proposes a novel multi-modal probabilistic topic model (LSTIT) to infer consumer preferences by jointly leveraging textual and visual data. Specifically, we use the title and image of the items purchased by consumers. Considering that the titles of items are relatively short text, we thus restrict the topic assignment for these titles. Meanwhile, we employ the same topic distribution to model the relationship between the title and the image of the item. To learn consumer preferences, the proposed model extracts several important dimensions based on textual words in titles and visual features in images. Experiments on the Amazon dataset show that the proposed model outperforms other baseline models for the task of learning consumer preferences. Our findings provide significant implications for managers to understand users’ personalized interests behind purchase behavior from a fine-grained level and a multi-modal perspective.

The purpose of group recommendation is to recommend items that all users in a group may like; therefore, the modeling target of group recommendation is not individual preference features, but group preference features, which are very complex in the context of online social platforms. In this paper, in order to better model group preferences, We propose the model Knowledge graph-based Multi-view Attention Group Recommendation (KMAGR) to model the group preference relationship in three aspects: 1) adding knowledge mapping relationships as side information to the model; 2) using attention mechanisms and graph neural network structures to model group purchase intentions; 3) in addition to modeling group preferences from the user’s perspective, we use group-user and group-intent multiple perspectives to model group preferences. We conducted experiments on two real online social datasets, and the experimental results proved that KMAGR outperformed other state-of-the-art models in group recommendation. Adding knowledge graph information and identifying group intent to the group recommendation system can greatly improve the effectiveness of group recommendation, while the critical path aggregation mechanism improves the explainability of recommendation results.