Papers with LLM-based recommendation models
UP5: Unbiased Foundation Model for Fairness-aware Recommendation (2024.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are gaining a foothold in Recommender Systems (RS) but there is growing concern that LLMs perpetuate stereotypes and may result in unfair recommendations. |
| Approach: | They propose a counterfactually-fair-prompt method for LLM-based recommendation that is based on unbiased foundation mOdels. |
| Outcome: | The proposed method achieves better recommendation performance with a high level of fairness on two real-world datasets. |
Stealthy Attack on Large Language Model based Recommendation (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in recommender systems have been overlooked due to their emphasis on textual content. |
| Approach: | They propose to introduce large language models into recommendation models to exploit the semantic understanding and strong transferability of LLMs. |
| Outcome: | The proposed approach significantly boosts an item’s exposure by altering its textual content during the testing phase, without requiring direct interference with the model’s training process. |