Comments (2)
LightFM itself simply computes the model. How you serve it in production is a different matter, and one that will be specific to each application. For example, you could precompute recommendations for all users and save them somewhere for later retrieval.
I'm not very familiar with predictionIO so will not be able to comment there.
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Ok thanks. Actually:
- I found your article http://ceur-ws.org/Vol-1448/paper4.pdf which gives some clues regarding production setup.
- From what I've seen it seems predictionio has 'hybrid' planned but not yet there.
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Related Issues (20)
- Recommendation for users the new items in the dataset HOT 1
- Features importance in lightFM
- Lightfm 1.17 - kernel dies in Jupyter, Spyder when fitting HOT 3
- How to reproduce the evaluation score in the paper.
- Recommender System with RFM ratings to put into Interaction matrix HOT 9
- Feature Request: Support for Freezing Item Embeddings while Updating User Embeddings During Training
- Incorporating item and user biases in LightFM embeddings for accurate recommendations in Vespa HOT 1
- Item and User Normalization
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- Evaluate AUC on a subset of items
- Auc evalutation return always nan
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- Implementing Incremental Training and Feedback loop using Lightfm
- LightFM kills Jupyter Kernel when fitting model
- Does calculating precision@k for one user makes sense? HOT 1
- Creating recommendations for single users without leveraging user identity features
- Can't install on Python 3.12 HOT 6
- Can't seem to install with with poetry HOT 3
- ModuleNotFoundError: No module named 'lightfm._lightfm_fast_no_openmp'
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