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Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

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Page 1: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Improvement of user-based CF

by using spectral clustering techniques

Page 2: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Context: cluster-based CF

neighbours identified based on a clustering method

Spectral Clustering: Normalised Cut

Page 3: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Context: cluster-based CF

neighbours identified based on a clustering method

Spectral Clustering: Normalised Cut

Better than k-Means: performance

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Page 4: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Context: cluster-based CF

neighbours identified based on a clustering method

Spectral Clustering: Normalised Cut

Better than k-Means: coverage

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Page 5: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Context: cluster-based CF

neighbours identified based on a clustering method

Spectral Clustering: Normalised Cut

Better than k-Means

Also better than MF and standard UB

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Page 6: Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

ACM Conference on Recommender Systems 2012 – Poster slam

September 11, Dublin, Ireland

Using Graph Partitioning Techniques

for Neighbour Selection in

User-Based Collaborative Filtering

Alejandro Bellogín Javier Parapar

Information Retrieval Group Information Retrieval Lab

Universidad Autónoma de Madrid University of A Coruña