Renata Ghisloti – ISEP22/12/10
Outline  Open Sorce Recommender System  Hybrid Recommender Systems: Survey and Experiments  Clustering Items for Collab...
Open Source Recommender          System  Daniel Lemire’s Project      PHP      Item-based Collaborative Filtering     ...
Hybrid Recommender Systems:    Survey and Experiments  Describes the five types of recommender systems  Proposes the hyb...
Hybrid Recommender Systems:              Survey and Experiments 1.   Weighted : linear combination of recomentations 2.   ...
Hybrid Recommender Systems:              Survey and Experiments22/12/10
Clustering Items for Collaborative                        Filtering      Experiments on Clustering Items      Better sca...
Clustering Approach for Hybrid               Recommender System      Integrate content information into a collaborative f...
Clustering Approach for Hybrid               Recommender System     1.    Apply the clustering in the items. Representatio...
Clustering Approach for Hybrid Recommender System Vs.      Content-Boosted Collaborative Filtering for Improved           ...
A Multi-Clustering Hybrid            Recommender System22/12/10
http://www.vogoo-api.com/http://www.daniel-lemire.com/fr/abstracts/TRD01.htmlhttp://lucene.apache.org/mahout/Mark O’Connor...
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Hybrid recommender systems

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Hybrid recommender systems

  1. 1. Renata Ghisloti – ISEP22/12/10
  2. 2. 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 System22/12/10
  3. 3. 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  Documentation22/12/10
  4. 4. Hybrid Recommender Systems: Survey and Experiments  Describes the five types of recommender systems  Proposes the hybrid method to overcome the problems 1. Weighted 2. Switching 3. Mixed 4. Feature Combination 5. Cascade 6. Feature Augmentation 7. Meta-level22/12/10
  5. 5. 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 another22/12/10
  6. 6. Hybrid Recommender Systems: Survey and Experiments22/12/10
  7. 7. Clustering Items for Collaborative Filtering  Experiments on Clustering Items  Better scalability  Relatively small lost in the accuracy (10%)22/12/10
  8. 8. Clustering Approach for Hybrid Recommender System  Integrate content information into a collaborative filtering  Clustering items  Tries to solve the cold start problem22/12/10
  9. 9. 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 start22/12/10
  10. 10. Clustering Approach for Hybrid Recommender System Vs. Content-Boosted Collaborative Filtering for Improved Recommendations  Clustering items by their  Makes an content-based content prediction on items that  Creates a new “rating have not been rated matrix”  Final rating is a mix of the  Final rating is a linear two sets of ratings combination of the two sets of ratings22/12/10
  11. 11. A Multi-Clustering Hybrid Recommender System22/12/10
  12. 12. 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 CollaborativeFilteringRobin Burke. Hybrid Recommender Systems: Survey andExperimentsQing Li, Byeong Man Kim. Clustering Approach for HybridRecommender SystemSutheera Puntheeranurak, Hidekazu Tsuji. A Multi-Clustering HybridRecommender System22/12/10

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