Optimize recommend() with bulk prediction
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by pre-computing an approximated user-item matrix
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Codecov ReportBase: 80.30% // Head: 80.37% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #64 +/- ##
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+ Coverage 80.30% 80.37% +0.06%
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Files 26 26
Lines 853 856 +3
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+ Hits 685 688 +3
Misses 168 168
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Since an entire approximated matrix is stored in a recommender, making bulk prediction can be efficient by extracting values of given indices.
Avoid multithreading for simplicity; builk predict is now called a thread that calls `recommend`, and hence nested thread spawns many cause complication.
Otherwise, aggregated metric may take an incomplete list of top-k recs containing undef.
takuti
changed the title
recommend() with bulk prediction
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In particular, accelerate calculating matrix factorization-based recommendation scores by pre-computing (caching) an approximated user-item matrix.