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Found 5 papers, 4 papers with code
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests.
LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}.
Uncovering ChatGPT's Capabilities in Recommender Systems
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
