A brief survey of machine learning and deep learning techniques for e-commerce research
The rapid growth of e-commerce has significantly increased the demand for advanced
techniques to address specific tasks in the e-commerce field. In this paper, we present a …
techniques to address specific tasks in the e-commerce field. In this paper, we present a …
Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect
T Hewei, L Youngsook - Entertainment computing, 2022 - Elsevier
This paper proposes a study on the impact of social e-commerce fashion products on
continuous purchase intention, and explores the relationship between social media …
continuous purchase intention, and explores the relationship between social media …
A deep learning based trust-and tag-aware recommender system
Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …
learning, and social networks to help users select their desired items. Collaborative filtering …
Deep learning-based collaborative filtering recommender systems: A comprehensive and systematic review
Nowadays, the volume of online information is growing and it is difficult to find the required
information. Effective strategies such as recommender systems are required to overcome …
information. Effective strategies such as recommender systems are required to overcome …
Factors affecting clothing purchase intention in mobile short video app: Mediation of perceived value and immersion experience
T Hewei - Plos one, 2022 - journals.plos.org
Based on the stimulus-organism-response (SOR) framework, this research introduces
perceived value and immersive experience, and builds a model of media interaction …
perceived value and immersive experience, and builds a model of media interaction …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …
DHSIRS: a novel deep hybrid side information-based recommender system
Latent factor-based methods have been extensively employed in recommender systems to
project users and items to the same feature space and use the dot product for predicting …
project users and items to the same feature space and use the dot product for predicting …
Explainable recommendations with nonnegative matrix factorization
X Zhang, X Zhou, L Chen, Y Liu - Artificial Intelligence Review, 2023 - Springer
Explicable recommendation system is proved to be conducive to improving the
persuasiveness of the recommendation system, enabling users to trust the system more and …
persuasiveness of the recommendation system, enabling users to trust the system more and …
Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations
Recently, cross-domain recommendation systems have been very helpful in improving the
quality of recommendation and solving the problem of cold start and data sparsity. Cross …
quality of recommendation and solving the problem of cold start and data sparsity. Cross …
A survey of learning-based methods for cold-start, social recommendation, and data sparsity in e-commerce recommendation systems
S Yin, X Luo - 2021 16th International Conference on …, 2021 - ieeexplore.ieee.org
With the continuous development of the economy and technology, people more and more
rely on online shopping, especially during the pandemic of COVID19. On the other hand …
rely on online shopping, especially during the pandemic of COVID19. On the other hand …