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 comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications
The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …
A survey on reinforcement learning for recommender systems
Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …
find useful information. In particular, reinforcement learning (RL)-based recommender …
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …
demands, driven by the growing number of users and items, pose a significant challenge to …
Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning
Learning activities are considerably supported and improved by the rapid advancement of e-
learning systems. This gives students a tremendous chance to participate in learning …
learning systems. This gives students a tremendous chance to participate in learning …
Harnessing large language models for text-rich sequential recommendation
Recent advances in Large Language Models (LLMs) have been changing the paradigm of
Recommender Systems (RS). However, when items in the recommendation scenarios …
Recommender Systems (RS). However, when items in the recommendation scenarios …
STRec: Sparse transformer for sequential recommendations
With the rapid evolution of transformer architectures, researchers are exploring their
application in sequential recommender systems (SRSs) and presenting promising …
application in sequential recommender systems (SRSs) and presenting promising …
Boss: A bilateral occupational-suitability-aware recommender system for online recruitment
X Hu, Y Cheng, Z Zheng, Y Wang, X Chi… - Proceedings of the 29th …, 2023 - dl.acm.org
With the rapid development of online recruitment platforms, a variety of emerging
recommendation services have been witnessed for benefiting both job seekers and …
recommendation services have been witnessed for benefiting both job seekers and …
Towards fair and personalized federated recommendation
Recommender systems have gained immense popularity in recent years for predicting
users' interests by learning embeddings. The majority of existing recommendation …
users' interests by learning embeddings. The majority of existing recommendation …