Method
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Item-based neighborhood approach - submitted by kidzik 3512 views, 0 downloads, 0 comments
last edited by kidzik - Sep 13, 2011, 20:09 CET Rating
- Summary:
Method for collaborative-filtering tasks. We base the rating of item for user by checking ratings of users with similar tastes
- License: CC-BY-SA 3.0
- Tags: collaborative-filtering item-based neighborhood recommender systems
- Results: 0 results
- Summary:
Method for collaborative-filtering tasks. We base the rating of item for user by checking ratings of users with similar tastes
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KDDCup'11 track2 by SVDFeature - submitted by crowwork 3863 views, 0 downloads, 0 comments
last edited by crowwork - Nov 2, 2011, 09:54 CET Rating
- Summary:
We describe our experiment on KDDCup'11 track2 dataset using SVDFeature, getting state-of-art single model peformance
- License: CC-BY-SA 3.0
- Tags: collaborative-filtering collaborative-ranking kddcup2011 SVDFeature
- Results: 0 results
- Summary:
We describe our experiment on KDDCup'11 track2 dataset using SVDFeature, getting state-of-art single model peformance
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Recommends via matrix completion - submitted by kidzik 3006 views, 0 downloads, 0 comments
last edited by kidzik - Sep 13, 2011, 20:16 CET Rating
- Summary:
More mathematical approach to recommendation problem. No additional data about items and users is needed - just incomplete matrix.
- License: CC-BY-SA 3.0
- Tags: collaborative-filtering low-rank matrices matrix-completion recommendation
- Results: 0 results
- Summary:
More mathematical approach to recommendation problem. No additional data about items and users is needed - just incomplete matrix.
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User-based neighborhood approach - submitted by kidzik 3956 views, 0 downloads, 0 comments
last edited by kidzik - Sep 13, 2011, 20:08 CET Rating
- Summary:
Method for collaborative-filtering tasks. We base the rating of item for user by checking ratings of users with similar tastes
- License: CC-BY-SA 3.0
- Tags: collaborative-filtering neighborhood recommender systems user-based
- Results: 0 results
- Summary:
Method for collaborative-filtering tasks. We base the rating of item for user by checking ratings of users with similar tastes
Disclaimer
We are acting in good faith to make datasets submitted for the use of the scientific community available to everybody, but if you are a copyright holder and would like us to remove a dataset please inform us and we will do it as soon as possible.
Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.