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 …
[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 …
Generative diffusion models on graphs: Methods and applications
Diffusion models, as a novel generative paradigm, have achieved remarkable success in
various image generation tasks such as image inpainting, image-to-text translation, and …
various image generation tasks such as image inpainting, image-to-text translation, and …
Fairness in recommendation: Foundations, methods, and applications
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …
playing an important role on assisting human decision-making. The satisfaction of users and …
Fairly adaptive negative sampling for recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit
feedback data, which usually learns user preference by discriminating between positive (ie …
feedback data, which usually learns user preference by discriminating between positive (ie …
Cheatagent: Attacking llm-empowered recommender systems via llm agent
Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have
brought significant advances in personalized user experience and have attracted …
brought significant advances in personalized user experience and have attracted …