RecAI and AdaRec

RecAI
55
Established
AdaRec
41
Emerging
Maintenance 10/25
Adoption 10/25
Maturity 16/25
Community 19/25
Maintenance 10/25
Adoption 4/25
Maturity 13/25
Community 14/25
Stars: 1,063
Forks: 114
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 6
Forks: 3
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
No Package No Dependents
No Package No Dependents

About RecAI

microsoft/RecAI

Bridging LLM and Recommender System.

RecAI helps e-commerce managers, content curators, and platform strategists build more intelligent and interactive recommendation systems. It takes various forms of data, like user interactions and item descriptions, and uses large language models to generate personalized product recommendations, content suggestions, or tailored user experiences. The output is a more sophisticated and explainable recommendation engine.

e-commerce recommendations content discovery personalization engine customer engagement AI agent development

About AdaRec

amazon-science/AdaRec

Adaptive Generative Recommendations with Large Language Models

AdaRec helps marketing and sales teams predict how individual customers will react to different promotions or product recommendations. It takes your customer's past behavior and profile data to generate rich natural language descriptions of them. This allows it to predict outcomes like purchase likelihood under various scenarios and suggest ideal products, helping you tailor your customer engagement strategies more effectively.

customer-segmentation marketing-campaigns product-recommendations sales-forecasting customer-behavior-analysis

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