hybrid recommender systems

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  • 1. Renata Ghisloti ISEP22/12/10

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. Open Source RecommenderSystem Daniel Lemires 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. 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. Hybrid Recommender Systems:Survey and Experiments 1. Weighted : linear combination of recomentations 2.Switching : the system uses some criterion to switch betweenrecommendation 3. Mixed: use several techniques and present them together 4. Feature Combination: use features from different techniques intoone algorithim 5. Cascade: one technique refines the other 6. Feature Augmentation: output from one technique as feature ofanother 7. Meta-level: model of one technique as input of another22/12/10 6. Hybrid Recommender Systems:Survey and Experiments22/12/10 7. Clustering Items for CollaborativeFiltering Experiments on Clustering Items Better scalability Relatively small lost in the accuracy (10%)22/12/10 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. 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. Clustering Approach for Hybrid Recommender System Vs.Content-Boosted Collaborative Filtering for ImprovedRecommendations 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 lineartwo sets of ratings combination of the two sets of ratings22/12/10 11. A Multi-Clustering HybridRecommender System22/12/10 12. http://www.vogoo-api.com/http://www.daniel-lemire.com/fr/abstracts/TRD01.htmlhttp://lucene.apache.org/mahout/Mark OConnor , 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