Api refactor #494
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Api refactor #494
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Change recommend/rank_items/similar_items/similar_users to return a numpy array of ids and a numpy array of scores, rather than return a python list of (itemid, score) tuples. This opens up options for batch processing in the future, and can be trivially converted by users into the previous format (liked ```zip(*model.recommend(...))```.
Previously, model.fit took an item_users sparse matrix, while model.recommend took a user_items sparse matrix. This was a source of confusion, so change the model.fit method to be consistent with model.recommend
Add batch mode operations for recommend, add an item list parameter and deprecate the rank_items and recommend_all methods.
benfred
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Approximate nearest neighbours used to only work for the ALS mode on the CPU. This change makes it so that we can compose ANN methods with any matrix factorization model (including BPR/LMF) and also use the GPU MF models as well. Currently this provides the same api in implicit/approximate_als.py for backwards compatibility - but this may be removed at a future date. Closes #487
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