Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

Author:

Sanner Scott1ORCID,Balog Krisztian2ORCID,Radlinski Filip3ORCID,Wedin Ben4ORCID,Dixon Lucas5ORCID

Affiliation:

1. University of Toronto, Canada

2. Google, Norway

3. Google, United Kingdom

4. Google, USA

5. Google, France

Publisher

ACM

Reference45 articles.

1. Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry Quoc Le and Charles Sutton. 2021. Program Synthesis with Large Language Models. arxiv:2108.07732 [cs.PL] Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry Quoc Le and Charles Sutton. 2021. Program Synthesis with Large Language Models. arxiv:2108.07732 [cs.PL]

2. Transparent, Scrutable and Explainable User Models for Personalized Recommendation

3. On Interpretation and Measurement of Soft Attributes for Recommendation

4. Defining and Supporting Narrative-driven Recommendation

5. Vadim Borisov Kathrin Seßler Tobias Leemann Martin Pawelczyk and Gjergji Kasneci. 2023. Language Models are Realistic Tabular Data Generators. arxiv:2210.06280 [cs.LG] Vadim Borisov Kathrin Seßler Tobias Leemann Martin Pawelczyk and Gjergji Kasneci. 2023. Language Models are Realistic Tabular Data Generators. arxiv:2210.06280 [cs.LG]

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Tell me what you Like: introducing natural language preference elicitation strategies in a virtual assistant for the movie domain;Journal of Intelligent Information Systems;2023-12-12

2. Trustworthy Recommender Systems;ACM Transactions on Intelligent Systems and Technology;2023-10-13

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