sileod/language-model-recommendation

Resources accompanying the "Zero-Shot Recommendation as Language Modeling" paper (ECIR2022)

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Emerging

This project helps researchers and practitioners evaluate how well large language models can recommend items without needing specific training data. You provide a dataset of past user interactions or preferences, and the project outputs insights into how effectively a language model could suggest new items. It's designed for data scientists and AI researchers exploring novel recommendation techniques.

No commits in the last 6 months.

Use this if you are a researcher or data scientist interested in leveraging large language models for recommendation tasks and want to benchmark their zero-shot capabilities against traditional methods.

Not ideal if you are looking for a ready-to-deploy, production-grade recommendation system or a tool for general machine learning model evaluation.

recommender-systems language-models AI-research zero-shot-learning data-science
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 10 / 25

How are scores calculated?

Stars

14

Forks

2

Language

Jupyter Notebook

License

Apache-2.0

Last pushed

May 25, 2023

Commits (30d)

0

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curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/sileod/language-model-recommendation"

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