A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
arXiv preprint arXiv:2309.11805 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper describes the organization, tasks, datasets, and participation results for the TalentCLEF 2026 challenge, which received 113 team registrations and over 400 submissions.
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
The paper describes the organization, tasks, datasets, and participation results for the TalentCLEF 2026 challenge, which received 113 team registrations and over 400 submissions.