Comments (3)
Michael Ekstrand [email protected] on 2011-06-30 14:18:52 said:
In [90bd7900c033dccbc3cbeb5d5cb89a7903ceedfe]:
Rework the vector framework for user and item vectors
* Introduce UserVector, ItemVector, and rating-specific subclasses
* Generalize `VectorNormalizer` to support normalizing various vectors (refs #97)
* Rework recommender interface to use `UserRatingVector` rather than user ID and rating vector
* Introduce `freeze()` method to make immutable vectors from mutable ones (this is instead of implicit copy-on-write; closes #50)
* Update release notes for all of this
Note: This comment has been automatically migrated from Bitbucket
Created by grouplens on 2013-02-01T21:55:54.266596+00:00, last updated: None
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ekstrand on 2011-06-20 18:51:16 said:
Would like to test this for 0.2.
Note: This comment has been automatically migrated from Bitbucket
Created by grouplens on 2013-02-01T21:55:53.906519+00:00, last updated: None
from lenskit.
ekstrand on 2011-05-24 18:06:10 said:
To do this, we should implement it in a separate branch and performance-test it.
Note: This comment has been automatically migrated from Bitbucket
Created by grouplens on 2013-02-01T21:55:53.566581+00:00, last updated: None
from lenskit.
Related Issues (20)
- Support query/runtime data in train-test evaluation
- Support emitting query data from crossfolder
- Support Bellogin's evaluation methods
- Bad import detection is broken HOT 1
- Add option for evaluation to continue after a failed job
- Add setting to restrict parallel evaluations
- Create general-purpose score/recommend/rank APIs
- which algorithm does use the item feature(e.g. some features in ML-100k's u.item files) in Lenskit HOT 3
- Support frequency-based recommendation
- Implement hit rate metric
- Rating summary is asking for Rating entities for implicit feedback data HOT 5
- Isolated train-test sets do not work correctly
- Implement new-style JDBC DAO HOT 2
- Write eval results to a database
- Adding Parameter to IntelliJ IDEA HOT 1
- Investigate switching to LA4J HOT 3
- Remove SparseVector
- Create general-purpose Lucene-based recommender HOT 1
- Support count attributes in popularity statistics
- ItemRecommender documentation is vague on some details. HOT 4
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