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---
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: "According to the purchase history of JSB727 : \n 2440 -> 1212 -> 4234 -> 4309 \n Can you recommend the next possible item to the user ?"
example_title: "Sequential Recommendation"
- text: "We want to make recommendation for user_823 . Select the best item from these candidates : \n 1402 , 6122 , 10240 , 84 , 10691 , 10944 , 3451 , 10213 , 4239 , 9018 , 3384 , 1397 , 7232 , 8221 , 7751 , 5397 , 11253 , 5078 , 3231 , 5105 , 3959 , 7245 , 150 , 2253 , 764 , 2646 , 6909 , 1939 , 1982 , 2201 , 11503 , 11603 , 4050 , 9524 , 1895 , 2317 , 11027 , 6971 , 4441 , 2336 , 373 , 11735 , 3824 , 51 , 9514 , 2488 , 7960 , 7653 , 2017 , 6541 , 3682 , 1064 , 910 , 6117 , 4556 , 3939 , 7049 , 241 , 10113 , 1442 , 10149 , 5930 , 7638 , 1140 , 11589 , 3935 , 11178 , 10491 , 8361 , 11266 , 2029 , 8965 , 4118 , 7506 , 3465 , 4283 , 2812 , 5465 , 2557 , 7510 , 2243 , 10292 , 1212 , 11419 , 3877 , 4771 , 3267 , 8508 , 10200 , 4048 , 3592 , 10145 , 4419 , 9583 , 6193 , 9257 , 1365 , 4672 , 9772 , 11440"
example_title: "Direct Recommendation"
- text: "Based on the feature word kids , generate an explanation for user_12107 about this product : Tumblin' Monkeys Game"
example_title: "Explanation Generation"
---
# P5 (Toys Base)
Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm
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