readme: 
  ---
  language: 
    - en
  
  tags:
  - sequential-recommendation
  - direct-recommendation
  - explanation-generation
  - text2text-generation
    
  license: mit
  
  datasets:
  - amazon_us_reviews
  
  metrics:
  - NDCG
  - HR
  - MAE
  - BLUE
  - ROUGE
  
  widget:
  - text: "I find the purchase history list of user_823 : \n 5255 -> 3001 -> 3771 -> 2973 \n I wonder what is the next item to recommend to the user . Can you help me decide ?"
    example_title: "Sequential Recommendation"
  - text: "Pick the most suitable item from the following list and recommend to user_182 : \n 5871 , 3575 , 6355 , 3665 , 7968 , 1054 , 11837 , 9031 , 2643 , 3125 , 11476 , 1529 , 6300 , 11755 , 9410 , 1578 , 5953 , 5042 , 10881 , 2221 , 11286 , 10458 , 2081 , 3722 , 10581 , 5879 , 1780 , 7411 , 5202 , 2082 , 82 , 8131 , 77 , 8097 , 1053 , 9946 , 1341 , 4508 , 2613 , 629 , 4869 , 9833 , 7076 , 6178 , 6679 , 6650 , 472 , 8821 , 4005 , 4184 , 2866 , 4988 , 10759 , 6358 , 4137 , 790 , 5390 , 9330 , 3691 , 2667 , 5620 , 11982 , 4799 , 10062 , 4278 , 4530 , 7944 , 10225 , 1766 , 6657 , 11371 , 305 , 1091 , 7144 , 1869 , 744 , 295 , 91 , 6947 , 9290 , 2977 , 11206 , 1677 , 7812 , 1159 , 1128 , 8762 , 5795 , 8061 , 9639 , 6161 , 2142 , 8124 , 5316 , 10425 , 12097 , 476 , 5710 , 1802 , 8969"
    example_title: "Direct Recommendation"
  - text: "Based on the feature word shampoo , generate an explanation for user_837 about this product : Dove Nourishing Oil Shampoo, 25.4 Ounce"
    example_title: "Explanation Generation"
  ---
  
  # P5 (Beauty Small)
  Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm 
  
  ![model image](https://raw.githubusercontent.com/jeykigung/P5/main/pic/teaser.png)