RecTools

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### Feature Description Realisation of [SLIM: Sparse Linear Methods for Top-N Recommender Systems](http://glaros.dtc.umn.edu/gkhome/node/774) ### Why this feature? Strong collaborative filtering baseline ### Additional context _No response_

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### Feature Description Metric to measure intersection in user-item (or item-item) pairs between recommendation lists. ### Why this feature? It helps for both candidate-generators selection in pipelines, and for popularity...

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### Feature Description Realisation of HitRate metric ### Why this feature? Classic RecSys metric ### Additional context _No response_

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### Feature Description Support for different scenarios in our validation pipelines ### Why this feature? Real world challenge ### Additional context _No response_

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### Feature Description I2I validation on meta-vectors distances between target item and recommended items ### Why this feature? One of the approaches to item-to-item validation ### Additional context _No response_

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### Feature Description Given test interactions, we can exclude all Positives that would have been recommended by the reference model (e.g. popular model). After that we can calculate Recall as...

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Widgets for simultaneous dual metrics analysis

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### Feature Description Plotly scatterplot widgets with functionality to select metrics for axes and hue from model parameters ### Why this feature? Great way to find pareto-optimum decisions in case...

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### Feature Description `add_holdout_fold` parameter for splitters. default is `False` `run_on_holdout_fold` parameter in `cross-validate`. default is `False` ### Why this feature? Holdout validation is an import part of experiments. It's...

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Copy existing Jupyter notebooks to Google Colab and add links to Readme and docs

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Add some functions to load commonly used recommendation datasets like `movielens`, `lastfm`, `kion`, etc. Think about: - Caching (should we implement saving loaded data? if yes, then how to do...

feldlime