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 historical perspective of explainable Artificial Intelligence
Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …
research by the need of conveying safety and trust to users in the “how” and “why” of …
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 …
Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …
recommendation accuracy and explainability. However, existing methods largely assume …
Improving knowledge-aware recommendation with multi-level interactive contrastive learning
Incorporating Knowledge Graphs (KG) into recommeder system as side information has
attracted considerable attention. Recently, the technical trend of Knowledge-aware …
attracted considerable attention. Recently, the technical trend of Knowledge-aware …
Make it a chorus: knowledge-and time-aware item modeling for sequential recommendation
Traditional recommender systems mainly aim to model inherent and long-term user
preference, while dynamic user demands are also of great importance. Typically, a historical …
preference, while dynamic user demands are also of great importance. Typically, a historical …
A reinforcement learning framework for explainable recommendation
Explainable recommendation, which provides explanations about why an item is
recommended, has attracted increasing attention due to its ability in helping users make …
recommended, has attracted increasing attention due to its ability in helping users make …
[HTML][HTML] A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources
As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …
out irrelevant information for users and provides them items that they may be interested in. In …
A knowledge-aware attentional reasoning network for recommendation
Abstract Knowledge-graph-aware recommendation systems have increasingly attracted
attention in both industry and academic recently. Many existing knowledge-aware …
attention in both industry and academic recently. Many existing knowledge-aware …