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 …
A review-aware graph contrastive learning framework for recommendation
Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …
and item embedding learning, followed by the user-item interaction modeling. By utilizing …
Rethinking the evaluation for conversational recommendation in the era of large language models
The recent success of large language models (LLMs) has shown great potential to develop
more powerful conversational recommender systems (CRSs), which rely on natural …
more powerful conversational recommender systems (CRSs), which rely on natural …
Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
BLoG: Bootstrapped graph representation learning with local and global regularization for recommendation
With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …
recommender systems has received considerable attention. Since user–item interactions …
Automated self-supervised learning for recommendation
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …
collaborative filtering (CF). To improve the representation quality over limited labeled data …
[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 …
Trustworthy recommender systems
Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …
interests from a large catalogue. For a quite long time, researchers and practitioners have …
A comprehensive survey on multimodal recommender systems: Taxonomy, evaluation, and future directions
Recommendation systems have become popular and effective tools to help users discover
their interesting items by modeling the user preference and item property based on implicit …
their interesting items by modeling the user preference and item property based on implicit …
Cross-domain recommendation via user interest alignment
Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …