CATN: Cross-domain recommendation for cold-start users via aspect transfer network
In a large recommender system, the products (or items) could be in many different
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …
Shilling black-box recommender systems by learning to generate fake user profiles
C Lin, S Chen, M Zeng, S Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to the pivotal role of recommender systems (RS) in guiding customers toward the
purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this …
purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this …
RAKCR: Reviews sentiment-aware based knowledge graph convolutional networks for Personalized Recommendation
Y Cui, H Yu, X Guo, H Cao, L Wang - Expert Systems with Applications, 2024 - Elsevier
The recommendation algorithm is an important means to alleviate the information explosion
in the era of big data. There has been a great deal of research into the use of knowledge …
in the era of big data. There has been a great deal of research into the use of knowledge …
Attention-based adaptive memory network for recommendation with review and rating
Item recommendation has become a significant means to help people discover interesting
items. Meanwhile, plenty of reviews and ratings in recommender system can be utilized to …
items. Meanwhile, plenty of reviews and ratings in recommender system can be utilized to …
Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation
Review-based recommender systems explore semantic aspects of users' preferences by
incorporating user-generated reviews into rating-based models. Recent works have …
incorporating user-generated reviews into rating-based models. Recent works have …
A multi-task dual attention deep recommendation model using ratings and review helpfulness
Z Liu, B Yuan, Y Ma - Applied Intelligence, 2022 - Springer
The existing review-based recommendation methods usually employ the same model to
learn the review representation of users and items. However, for different user-item pairs, the …
learn the review representation of users and items. However, for different user-item pairs, the …
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity
problem. Content-based CDR is one of the most promising branches since most kinds of …
problem. Content-based CDR is one of the most promising branches since most kinds of …
Learning aspect-aware high-order representations from ratings and reviews for recommendation
Textual reviews contain rich semantic information that is useful for making better
recommendation, as such semantic information may indicate more fine-grained preferences …
recommendation, as such semantic information may indicate more fine-grained preferences …
based hierarchical attention cooperative neural networks for recommendation
Y Du, L Wang, Z Peng, W Guo - Neurocomputing, 2021 - Elsevier
In e-commerce platform, users conduct purchase behavior and write reviews for the
purchased items. These reviews usually contain a lot of valuable information for …
purchased items. These reviews usually contain a lot of valuable information for …
Multi-level attentive deep user-item representation learning for recommendation system
With the development of e-commerce platforms, user reviews have become a vital source of
information to address the sparsity problems for enhancing the predictive performance of the …
information to address the sparsity problems for enhancing the predictive performance of the …