Tools for recommendation systems development
Project description
ml-recsys-tools
Open source repo for various tools for recommender systems development work. Work in progress.
Main purpose is to provide a single API for various recommender packages to train, tune, evaluate and get data in and recommendations / similarities out.
Recommender models and tools:
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LightFM package based recommender.
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Spotlight package based implicit recommender.
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Implicit package based ALS recommender.
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Serving / Tuning / Evaluation features added for most recommenders:
- Dataframes for all inputs and outputs
- adding external features (for LightFM hybrid mode)
- early stopping fit (for iterative models: LightFM, ALS, Spotlight)
- hyperparameter search
- fast batched methods for:
- user recommendation sampling
- similar items samplilng with different similarity measures
- similar users sampling
- evaluation by sampling and ranking
- Dataframes for all inputs and outputs
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Additional recommender models:
-
Similarity based:
- cooccurence (items, users)
- generic similarity based (can be used with external features)
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Ensembles:
- subdivision based (multiple recommenders each on subset of data - e.g. geographical region):
- geo based: simple grid, equidense grid, geo clustering
- LightFM and cooccurrence based
- combination based - combining recommendations from multiple recommenders
- similarity combination based - similarity based recommender on similarities from multiple recommenders
- cascade ensemble
- subdivision based (multiple recommenders each on subset of data - e.g. geographical region):
-
Interaction dataframe and sparse matrix handlers / builders:
- sampling, data splitting,
- external features matrix creation (additional item features), with feature engineering: binning / one*hot encoding (via pandas_sklearn)
- evaluation and ranking helpers
- handlers for observations coupled with external features and features with geo coordinates
- mappers for geo features, observations, recommendations, similarities etc.
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Evaluation utils:
- score reports on lightfm metrics (AUC, precision, recall, reciprocal)
- n-DCG, and n-MRR metrics, n-precision / recall
- references: best possible ranking and chance ranking
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Utilities:
- hyperparameter tuning utils (by skopt)
- similarity calculation helpers (similarities, dot, top N, top N on sparse)
- parallelism utils
- sklearn transformer extenstions (for feature engineering)
- google maps util for displaying geographical data
- logging, debug printouts decorators and other isntrumentation and inspection tools
- pandas utils
- data helpers: redis, s3
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Examples:
- a basic example on movielens 1M demonstrating:
- basic data ingestion without any item/user features
- LightFM recommender: fit, evaluation, early stopping, hyper-param search, recommendations, similarities
- Cooccurrence recommender
- Two combination ensembles (Ranks and Simils)
- a basic example on movielens 1M demonstrating:
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Still to add:
- add example in README.MD
- add and reorganize examples
- much more comments and docstrings
- more tests
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