Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

**Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning**

Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya(*The work was done when Keertana interned at Netflix.)IntroductionThis blog focuses on post-training generative recommender systems. Generative recommenders (GRs) represent a new paradigm in the field of recommendation systems (e.g. These models draw inspiration from recent advancements in transformer architectures used for language and vision tasks. They approach the recommendation problem, including both ranking and retrieval, as a sequential transduction task. This perspective enables generative training, where the model learns by imitating the next event in a sequence of user activities, thereby effectively modeling user behavior over time.However, a key challenge with simply replicating observed user patterns is that it may not always lead to the best possible recommendations...

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