TY - GEN
T1 - Large Language Models for Generative Recommendation
T2 - Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024
AU - Li, Lei
AU - Zhang, Yongfeng
AU - Liu, Dugang
AU - Chen, Li
N1 - This work was supported by Hong Kong Baptist University IG-FNRA project (RC-FNRA-IG/21-22/SCI/01) and Hong Kong Research Grants Council (RGC) Postdoctoral Fellowship Scheme (PDFS2223-2S02), and partially supported by NSF IIS-1910154, 2007907, and 2046457. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.
Publisher Copyright:
© 2024 ELRA Language Resource Association: CC BY-NC 4.0.
PY - 2024/5
Y1 - 2024/5
N2 - Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.
AB - Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.
KW - Generative Recommendation
KW - Information Retrieval
KW - Large Language Models
KW - Recommender Systems
UR - https://aclanthology.org/2024.lrec-main.886
UR - http://www.scopus.com/inward/record.url?scp=85187768758&partnerID=8YFLogxK
M3 - Conference proceeding
AN - SCOPUS:85187768758
T3 - Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING - Main Conference Proceedings
SP - 10146
EP - 10159
BT - Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
A2 - Calzolari, Nicoletta
A2 - Kan, Min-Yen
A2 - Hoste, Veronique
A2 - Lenci, Alessandro
A2 - Sakti, Sakriani
A2 - Xue, Nianwen
PB - European Language Resources Association (ELRA)
Y2 - 20 May 2024 through 25 May 2024
ER -