Serendipity in recommender systems: a systematic literature review
A recommender system is employed to accurately recommend items, which are expected to
attract the user's attention. The over-emphasis on the accuracy of the recommendations can …
attract the user's attention. The over-emphasis on the accuracy of the recommendations can …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
Cross domain recommendation via bi-directional transfer graph collaborative filtering networks
M Liu, J Li, G Li, P Pan - Proceedings of the 29th ACM international …, 2020 - dl.acm.org
Data sparsity is a challenge problem that most modern recommender systems are
confronted with. By leveraging the knowledge from relevant domains, the cross-domain …
confronted with. By leveraging the knowledge from relevant domains, the cross-domain …
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 …
Attention-based dynamic user modeling and deep collaborative filtering recommendation
R Wang, Z Wu, J Lou, Y Jiang - Expert Systems with Applications, 2022 - Elsevier
Deep learning (DL) techniques have been widely used in recommender systems for user
modeling and matching function learning based on historical interaction matrix. However …
modeling and matching function learning based on historical interaction matrix. However …
GAF-Net: Graph attention fusion network for multi-view semi-supervised classification
Multi-view semi-supervised classification is a typical task to classify data using a small
amount of supervised information, which has attracted a lot of attention from researchers in …
amount of supervised information, which has attracted a lot of attention from researchers in …
Connecting user and item perspectives in popularity debiasing for collaborative recommendation
Recommender systems learn from historical users' feedback that is often non-uniformly
distributed across items. As a consequence, these systems may end up suggesting popular …
distributed across items. As a consequence, these systems may end up suggesting popular …
A Revisiting Study of Appropriate Offline Evaluation for Top-N Recommendation Algorithms
In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …
data. Although the recent success of deep learning largely pushes forward the research on …
Knowledge graph enhanced neural collaborative recommendation
Existing neural collaborative filtering (NCF) recommendation methods suffer from severe
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …
A deep neural collaborative filtering based service recommendation method with multi-source data for smart cloud-edge collaboration applications
W Lin, M Zhu, X Zhou, R Zhang, X Zhao… - Tsinghua Science …, 2023 - ieeexplore.ieee.org
Service recommendation provides an effective solution to extract valuable information from
the huge and ever-increasing volume of big data generated by the large cardinality of user …
the huge and ever-increasing volume of big data generated by the large cardinality of user …