implicit VS annoy

Although CF methods also have some explainability available. CF library https://github.com/benfred/implicit which I used a lot in my past projects, e.g. has the method model.explain available for that.

In that case, you are doing some form of collaborative filtering, though you can also add content-based filtering as additional features later. You can use either implicit or explicit feedback. I would suggest checking this package, and this tutorial. Let me know if you have any other questions.

It'd definitely be a nice-to-have. Luckily it shouldn't be to hard to create a custom estimator using something like Spotify's Annoy library. I might try it out whenever I come back and revisit the project.

Add your examples to the index and build the trees in annoy. I feel like its straight forward. There you have to provide the dimension of the features which is the feature vector you get. In my case I am reusing (without fine-tuning) the effecientNetB3 without the last layer. Hence it results in feature vectors with 1536 dimensions. https://github.com/spotify/annoy

Annoy: https://github.com/spotify/annoy

Approximate Nearest Neighbors is what Spotify uses for music recommendations: https://github.com/spotify/annoy

embeddings - The embeddings index file. This is an Approximate Nearest Neighbor (ANN) index with either Faiss (default), Hnswlib or Annoy, depending on the settings.

Take for example Spotifies implementation of ANN https://github.com/spotify/annoy.

I used for this project Flask and OpenAI CLIP. For the vector search I used approximate nearest neighbors provided by spotify/annoy. I used Flask-SQLAlchemy with GeoAlchemy2 to query GPS coordinates. The embedding was done using UMAP.

I’ve used annoy [0] for vector-similarity search before. Does anyone know the differences between Faiss and annoy? Thanks!

[0] https://github.com/spotify/annoy

🔎 Large-scale similarity search with multiple index backends (Faiss, Annoy, Hnswlib)