Release 0.6.0

This release provides a number of new features as well as performance improvements:

New features and convenience functions

• feedback parameter can now be omitted in RecommenderData instances, which simplifies work with purely implicit positive-only data;
• separate routine to unfold tensor along a specified mode;
• new random grid search routine random_grid in polara/evaluation/pipelines
• evaluation now allows for parallel execution on test data chunks; it helps to reduce evaluation time in certain cases;

Performance improvements

• tensor rounding is now a part of tensor model, allowing for efficient rank truncation (similarly to SVD) without the need to recompute the whole model;
• computing recommendation scores in the tensor model is now more efficient in terms of both memory and CPU load;
• better handling of iALS algorithm from implicit library; now in standard scenario instead of relying on inefficient recommend function, the evaluation is performed fully on polara side;

Other improvements

• get_movielens_data now allows to load tags and timestamp data;
• HR and MRR metrics con now be calculated independently of the number of holdout items;
• user defined memory usage limit is now a computed value, allowing for dynamic changes;
• many improvements on code readability and naming consistency;
• several bugfixes and a number of other improvements, mostly related to computational efficiency and general workflow control.