The document outlines several open source recommender systems and approaches to hybrid recommender systems. It discusses Daniel Lemire's PHP item-based collaborative filtering project, Apache Mahout which uses data mining algorithms for item and user-based collaborative filtering, and Vogoo which implements item and user-based collaborative filtering. Several types of hybrid recommender systems are described including weighted, switching, mixed, feature combination, cascade, feature augmentation, and meta-level. The document also summarizes research on clustering items for collaborative filtering and using clustering approaches for hybrid recommender systems to address cold start problems.

1. Renata Ghisloti – ISEP
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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 System
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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
 Documentation
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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-level
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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 another
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6. Hybrid Recommender Systems:
Survey and Experiments
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7. Clustering Items for Collaborative
Filtering
 Experiments on Clustering Items
 Better scalability
 Relatively small lost in the accuracy (10%)
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8. Clustering Approach for Hybrid
Recommender System
 Integrate content information into a collaborative filtering
 Clustering items
 Tries to solve the cold start problem
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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 start
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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 ratings
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11. A Multi-Clustering Hybrid
Recommender System
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12. http://www.vogoo-api.com/
http://www.daniel-lemire.com/fr/abstracts/TRD01.html
http://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
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