Comments (5)
I changed my config to
bind ItemScorer to ItemItemScorer within (UserVectorNormalizer) { bind VectorNormalizer to UnitVectorNormalizer } bind RatingVectorPDAO to EntityCountRatingVectorPDAO set InteractionEntityType to EntityType.forName("PAGEVIEW") bind (BaselineScorer, ItemScorer) to UserMeanItemScorer bind (UserMeanBaseline, ItemScorer) to ItemMeanRatingItemScorer
So this particular issue is solved but during recommendation but still at the time of creating recommendations following query gets executed
dao.query(CommonTypes.RATING) .withAttribute(CommonAttributes.USER_ID, user) .valueSet(CommonAttributes.ITEM_ID);
in TopNItemRecommender.java which is again causing issues.
from lenskit.
Thanks for raising this - I apologize for not noticing it earlier.
TopNRecommender
does need to be updated.
from lenskit.
The solution here is to make TopNItemRecommender
use InteractionEntityType
. We also need a test for this.
from lenskit.
Is there a possible configuration for implicit data that doesn't make use of TopNItemRecommender? Or can a fix for it be expected?
from lenskit.
You can implement your own ItemRecommender
, if you want.
Future LensKit development is in LKPY.
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
- 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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