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A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Contrastive learning for representation degeneration problem in sequential recommendation
Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …
have significantly facilitated the sequential recommendation. However, according to our …
Sequential recommendation with graph neural networks
Sequential recommendation aims to leverage users' historical behaviors to predict their next
interaction. Existing works have not yet addressed two main challenges in sequential …
interaction. Existing works have not yet addressed two main challenges in sequential …
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 …
Self-supervised hypergraph convolutional networks for session-based recommendation
Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …
point. As user profiles are generally not available in this scenario, capturing the user intent …
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 …
Global context enhanced graph neural networks for session-based recommendation
Session-based recommendation (SBR) is a challenging task, which aims at recommending
items based on anonymous behavior sequences. Almost all the existing solutions for SBR …
items based on anonymous behavior sequences. Almost all the existing solutions for SBR …
Graph learning based recommender systems: A review
Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …
based Recommender Systems (GLRS). GLRS employ advanced graph learning …
Self-supervised graph co-training for session-based recommendation
Session-based recommendation targets next-item prediction by exploiting user behaviors
within a short time period. Compared with other recommendation paradigms, session-based …
within a short time period. Compared with other recommendation paradigms, session-based …