# Which open-source model would you recommend for generating video recommendations in an OTT platform?

> Source: <https://discuss.huggingface.co/t/which-open-source-model-would-you-recommend-for-generating-video-recommendations-in-an-ott-platform/178458#post_2>
> Published: 2026-08-04 23:29:20+00:00

I would not start with a general-purpose LLM for this. Video recommendation is mainly a **retrieval-and-ranking** problem, and the best model depends on what interaction data you have.

A practical open-source starting point is **TensorFlow Recommenders (TFRS)** with a two-tower retrieval model:

The official movie-retrieval tutorial is close to this use case and includes training, evaluation, and export: [https://www.tensorflow.org/recommenders/examples/basic_retrieval](https://www.tensorflow.org/recommenders/examples/basic_retrieval)

If you have ordered watch histories, compare sequential models such as **BERT4Rec** or SASRec through RecBole. BERT4Rec is specifically designed to model sequences of user interactions: [BERT4Rec — RecBole 1.2.1 documentation](https://recbole.io/docs/user_guide/model/sequential/bert4rec.html)

For a cold-start baseline, you can embed titles, descriptions, genres, and tags with a text encoder such as `sentence-transformers/all-MiniLM-L6-v2`

and recommend semantically similar videos. Its model card lists semantic search as an intended use: [sentence-transformers/all-MiniLM-L6-v2 · Hugging Face](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) . However, that is an item-content encoder, not a complete personalized recommender.

My suggested progression would be:

So, if I had to choose one free starting stack: **TFRS two-tower retrieval plus a ranker**, with content embeddings for cold start. I would choose BERT4Rec only after confirming that sequential watch history improves the offline metrics.