Ethan00Si/Instrumental-variables-for-recommendation
The official implementation for WWW 2022 paper "A Model-Agnostic Causal Learning Framework for Recommendation using Search Data"
This project helps e-commerce and content platforms improve their recommendation systems by accounting for how user searches influence what they click on. It takes in user search queries and item interaction data, then outputs a more accurate understanding of what truly drives user engagement with recommended items. Recommendation system engineers and data scientists in online platforms would find this useful.
No commits in the last 6 months.
Use this if you need to build more effective recommendation engines by understanding the causal relationship between user searches and item interactions, moving beyond simple correlations.
Not ideal if you don't have access to both user search data and recommendation interaction data, or if you are looking for a general-purpose recommendation framework without a focus on causal inference.
Stars
31
Forks
4
Language
Python
License
—
Category
Last pushed
Jan 19, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Ethan00Si/Instrumental-variables-for-recommendation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of...
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research...
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
cdt15/lingam
Python package for causal discovery based on LiNGAM.
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python