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Renata Ghisloti – ISEP
22/12/10
Outline Open Sorce Recommender System
Hybrid Recommender Systems: Survey and Experiments
Clustering Items for Collaborative Filtering
Clustering Approach for Hybrid Recommender System
A Multi-Clustering Hybrid Recommender System
22/12/10
Open Source Recommender System
Daniel Lemire’s Project PHP Item-based Collaborative Filtering Slope-one creator
Apache Mahout JAVA Data Mining Algorithms Item-based Collaborative Filtering User-based Collaborative Filtering Good documentation
Vogoo PHP 2 Item-based Collaborative Filtering User-based Collaborative Filtering Documentation
22/12/10
Hybrid Recommender Systems:Survey and Experiments
Describes the five types of recommender systems Proposes the hybrid method to overcome the problems
1. Weighted2. Switching3. Mixed4. Feature Combination5. Cascade6. Feature Augmentation7. Meta-level
22/12/10
Hybrid Recommender Systems:Survey and Experiments
1. Weighted : linear combination of recomentations
2. Switching : the system uses some criterion to switch between
recommendation
3. Mixed: use several techniques and present them together
4. Feature Combination: use features from different techniques into
one algorithim
5. Cascade: one technique refines the other
6. Feature Augmentation: output from one technique as feature of
another
7. Meta-level: model of one technique as input of another
22/12/10
Hybrid Recommender Systems:Survey and Experiments
22/12/10
Clustering Items for Collaborative Filtering
Experiments on Clustering Items
Better scalability
Relatively small lost in the accuracy (10%)
22/12/10
Clustering Approach for Hybrid Recommender System
Integrate content information into a collaborative filtering
Clustering items
Tries to solve the cold start problem
22/12/10
Clustering Approach for Hybrid Recommender System
1. Apply the clustering in the items. Representation: fuzzy set.
2. Calculate the similairty of the fuzzy set and the original dating
data. Calculate the linear combination of both.
3. Prediction by the neighbours algorithm
Results:
Data from MovieLens
Comparition with Users-clustering and with pure Item-based
collaborative Filtering -> smaller MAE
Improvements for the cold start
22/12/10
Clustering Approach for Hybrid Recommender System Vs. Content-Boosted Collaborative Filtering for Improved
Recommendations
Clustering items by their content
Creates a new “rating matrix”
Final rating is a linear combination of the two sets of ratings
Makes an content-based prediction on items that have not been rated
Final rating is a mix of the two sets of ratings
22/12/10
A Multi-Clustering Hybrid Recommender System
22/12/10
http://www.vogoo-api.com/http://www.daniel-lemire.com/fr/abstracts/TRD01.htmlhttp://lucene.apache.org/mahout/
Mark O’Connor , Jon Herlocker. Clustering Items for Collaborative Filtering
Robin Burke. Hybrid Recommender Systems: Survey and Experiments
Qing Li, Byeong Man Kim. Clustering Approach for Hybrid Recommender System
Sutheera Puntheeranurak, Hidekazu Tsuji. A Multi-Clustering Hybrid Recommender System
22/12/10