China is currently the world’s largest cross-border e-commerce purchaser and destination country. Therefore, how to promote consumer online shopping is the most important goal for cross-border e-commerce sustainability. Meanwhile, the previous research has not empirically verified the precise effect of online shopping context and perceived value on consumers’ cross-border online purchase intention. To address this gap, this study analyzes the online shopping context that determines consumers’ purchase intention and innovatively identifies four cues that promote this consumption behavior in cross-border e-commerce, such as online promotion cues, content marketing cues, personalized recommendation cues, and social review cues. It proposes a theoretical model based on cue utilization theory and stimulus-organism-response model, which introduces this four cues and brand familiarity in analyzing the effects on consumers’ purchase intention in cross-border online shopping (CBOS). In addition, the paper examines the mediating role of perceived functional value and perceived emotional value. Survey data collected 372 cross-border online consumers from China and the PLS-SEM method was used to empirically test the proposed model. The results show that these four cross-border online shopping context cues have a significantly positive impact on consumers’ purchase intention. Brand familiarity has significantly negative moderating effects between the four cues and the perceived functional value, while brand familiarity also negatively moderates the relationship between online promotion cues, social review cues, and perceived emotional value, respectively.

A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users’ consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users’ privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce.

Supervising the environmental protection behavior of enterprises is a key strategy to achieve “carbon peaking and carbon neutrality”. This research innovatively proposes the concept of precise supervision, aiming to implement differentiated supervision measures for different types of enterprises, and realize the precise supervision method of enterprise environmental protection, which is different from the traditional supervision mode. Firstly, this paper proposes a novel MEBF+ method based on the benchmark algorithm MEBF, and obtains MEBF++ after incorporating the model bias. Secondly, based on the dataset of environmental supervision and certification of listed Chinese companies, the accuracy and robustness of the proposed method are verified by using multiple evaluation indicators. Finally, based on the analysis of the experimental results, two precise supervision concepts, narrow and broad, are proposed under the low-carbon background. The results show that compared with the benchmark method, the accuracy of the proposed method has been improved to a large extent. In addition, the precise supervision proposed in this paper can help reduce the consumption of manpower and resources as well as unite the public to monitor the environmental protection behavior of enterprises.

Compared with traditional e-commerce platforms, content-based e-commerce has obvious advantages in commodity display, as it can present commodities more comprehensively, three-dimensionally and flexibly, providing better user service experience. However, the micro-video format of content-based e-commerce, with its complex information and high real-time requirements, limits recommendation accuracy. Most existing micro-video recommendation methods focus on static user interest modeling, while few construct dynamic interest models via short-term interaction sequences to adapt to rapid preference changes. Moreover, most dynamic modeling studies ignore multi-dimensional transfer relationships in interactions, leading to interest perception biases in recommendation tasks. To address these issues, this study proposes a multi-dimensional sequence and graph neural network based micro-video recommendation algorithm (MSAGNN). Specifically, the algorithm first constructs user preference sequences into three-dimensional graphs (item, category, author) chronologically. These graphs are input into the graph neural network to extract multi-dimensional micro-video feature representations, which are then fused via a dedicated strategy. Each sequence item is assigned positional information and processed by the attention mechanism to obtain the final session representation, which is dot-multiplied with candidate items to generate recommendations. Extensive experiments on two KuaiRec datasets with different densities verify the effectiveness and rationality of MSAGNN.

Sequential recommendation provides an effective way to model users' historical behaviors for predicting future interactions. However, the existing methods have not fully explored how to conduct stable intent data augmentation based on user sub-intents, and simultaneously construct multi-view self-supervised signals for intent contrastive learning to assist in the next step of prediction. To address these limitations, we propose IeSRec, an intent-enhanced sequential recommendation model. It explicitly incorporates the original behavioral intentions of users by introducing three novel data augmentation strategies. These strategies construct diverse behavioral subsequences from multiple perspectives, thereby strengthening the representation of implicit intents. Based on operations such as segmentation, encoding, and clustering, we design a multi-view self-supervised framework to capture distinctions among different user intentions. Additionally, we formulate three contrastive learning objectives to bring users with similar intentions closer in the embedding space while pushing those with dissimilar intentions apart. The intent contrastive learning module is jointly optimized with the primary next-item prediction task in an end-to-end manner. Extensive experiments on six public datasets demonstrate that IeSRec consistently outperforms state-of-the-art baselines. Ablation studies further validate the effectiveness of the proposed data augmentation methods, and their portability to other sequential recommendation architectures.