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M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
[article]
2022
arXiv
pre-print
Industrial recommender systems have been growing increasingly complex, may involve diverse domains such as e-commerce products and user-generated contents, and can comprise a myriad of tasks such as retrieval, ranking, explanation generation, and even AI-assisted content production. The mainstream approach so far is to develop individual algorithms for each domain and each task. In this paper, we explore the possibility of developing a unified foundation model to support open-ended domains and
arXiv:2205.08084v2
fatcat:p645he3l7zht3dlrxngo5qcecq