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20232026
most citedTowards Empathetic Conversational Recommender Systems

21 citations · 27 across the 15 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.IR2024

The 1st Workshop on Human-Centered Recommender Systems

Kaike Zhang, Yunfan Wu, Yougang lyu +6

Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous chall…

cs.IR2024★ 2 cited

Cognitive Biases in Large Language Models for News Recommendation

Yougang Lyu, Xiaoyu Zhang, Zhaochun Ren +1

Despite large language models (LLMs) increasingly becoming important components of news recommender systems, employing LLMs in such systems introduces new risks, such as the influe…

cs.CL2024

MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization

Yougang Lyu, Lingyong Yan, Zihan Wang +4

As large language models (LLMs) are rapidly advancing and achieving near-human capabilities on specific tasks, aligning them with human values is becoming more urgent. In scenarios…

cs.IR2024★ 21 cited

Towards Empathetic Conversational Recommender Systems

Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7

Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…

cs.CL2024★ 1 cited

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models

Yougang Lyu, Lingyong Yan, Shuaiqiang Wang +6

Despite their success at many natural language processing (NLP) tasks, large language models still struggle to effectively leverage knowledge for knowledge-intensive tasks, manifes…