RQ-VAE-Recommender
[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"
Implement a generative retrieval model to recommend items based on user preferences using semantic IDs.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytime[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"
RQ-VAE-Recommender has 842 stars on GitHub. It has been forked 126 times. RQ-VAE-Recommender is written mainly in Python. It has been in active development since 2024. RQ-VAE-Recommender is available under the MIT license. Its main topics are generative, generative-ai, generative-retrieval, gumbel.
What is RQ-VAE-Recommender?
[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"
Is RQ-VAE-Recommender open source?
RQ-VAE-Recommender is an open-source project. It is released under the MIT license.
Is RQ-VAE-Recommender free?
Yes. RQ-VAE-Recommender is free and open source — you can use, modify and self-host it.
What license does RQ-VAE-Recommender use?
RQ-VAE-Recommender is available under the MIT license.
What language is RQ-VAE-Recommender written in?
RQ-VAE-Recommender is written mainly in Python.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/edoardobotta-rq-vae-recommender.html)
