Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences-Reference-Cited by-同舟云学术

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

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM Conference on Recommender Systems
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Author:

Sanner Scott¹^ORCID,Balog Krisztian²^ORCID,Radlinski Filip³^ORCID,Wedin Ben⁴^ORCID,Dixon Lucas⁵^ORCID

Affiliation:

1. University of Toronto, Canada

2. Google, Norway

3. Google, United Kingdom

4. Google, USA

5. Google, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3608845

Reference45 articles.

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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]

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