Graph neural networks in recommender systems: a survey
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …
alleviate such information overload. Due to the important application value of recommender …
A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation
Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …
understanding, research in recommendation has shifted to inventing new recommender …
Filter-enhanced MLP is all you need for sequential recommendation
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …
the task of sequential recommendation, which aims to capture the dynamic preference …
Learning intents behind interactions with knowledge graph for recommendation
Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …
recent technical trend is to develop end-to-end models founded on graph neural networks …
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …
learning. Existing neural sequential recommendation models usually rely on the item …
Contrastive learning for sequential recommendation
Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …
because of their ability to capture a user's dynamic interest from her/his historical inter …
[HTML][HTML] Knowledge graphs as tools for explainable machine learning: A survey
I Tiddi, S Schlobach - Artificial Intelligence, 2022 - Elsevier
This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …
Explainable Machine Learning. As of late, explainable AI has become a very active field of …
SimKGC: Simple contrastive knowledge graph completion with pre-trained language models
Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
Multi-level cross-view contrastive learning for knowledge-aware recommender system
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
Recently, graph neural networks (GNNs) based model has gradually become the theme of …