RecGPT: Generative Personalized Prompts for Sequential Recommendation
  via ChatGPT Training Paradigm
Yabin Zhang Wenhui Yu Erhan Zhang Xu Chen Lantao Hu Peng Jiang Kun Gai
ChatGPT has achieved remarkable success in natural language understanding.
Considering that recommendation is indeed a conversation between users and the
system with items as words, which has similar underlying pattern with ChatGPT,
we design a new chat framework in item index level for the recommendation task.
Our novelty mainly contains three parts: model, training and inference. For the
model part, we adopt Generative Pre-training Transformer (GPT) as the
sequential recommendation model and design a user modular to capture
personalized information. For the training part, we adopt the two-stage
paradigm of ChatGPT, including pre-training and fine-tuning. In the
pre-training stage, we train GPT model by auto-regression. In the fine-tuning
stage, we train the model with prompts, which include both the newly-generated
results from the model and the user's feedback. For the inference part, we
predict several user interests as user representations in an autoregressive
manner. For each interest vector, we recall several items with the highest
similarity and merge the items recalled by all interest vectors into the final
result. We conduct experiments with both offline public datasets and online A/B
test to demonstrate the effectiveness of our proposed method.