Renata Ghisloti – ISEP

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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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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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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

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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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Hybrid Recommender Systems:Survey and Experiments

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Clustering Items for Collaborative Filtering

Experiments on Clustering Items

Better scalability

Relatively small lost in the accuracy (10%)

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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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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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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

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A Multi-Clustering Hybrid Recommender System

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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

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