![Introduction Recommender systems: help users dealing with information overload Components of a recommender system [ Burke2002 ] Set of users, set of items (products) Implicit/explicit user rating on items Additional information: trust, collaboration, etc. Algorithms for generating recommendations Recommendation techniques [ Adomavicius and Tuzhilin 2005 ] Collaborative Filtering (CF) [Breese et al. 1998 ] Memory-based algorithms: user-based, item-based [Sarwar 2001] Model-based algorithms: Bayesian network [ Breese1998 ] ; Clustering [ Ungar 1998 ] ; Rule-based [ Sarwar2000 ] ; Machine learning on graphs [Zhou 2005, 2008]; PLSA [Hofmann 1999] ; Matrix factorization [Koren 2009] Content-based recommendation [Sarwar et al. 2001] Hybrid approaches [Burke 2002]](https://image.slidesharecdn.com/i-know2010pham-110725033918-phpapp01/85/Clustering-Technique-for-Collaborative-Filtering-Recommendation-and-Application-to-Venue-Recommendation-3-320.jpg)
![Clustering and Collaborative Filtering Cluster 2 Cluster 1 item-based CF User clustering Item clustering item-based CF item-based CF Problems: large-scale data; sparse rating matrix; diversity of users and items Previous approaches: Clustering based on ratings K-means, Metis, etc. [Rashid 2006, Xue 2005, O’Connor 2001] Our approach Clustering based on additional information: relationships between users, items Improvement on both efficiency and accuracy x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x](https://image.slidesharecdn.com/i-know2010pham-110725033918-phpapp01/85/Clustering-Technique-for-Collaborative-Filtering-Recommendation-and-Application-to-Venue-Recommendation-4-320.jpg)
![Data Sets DBLP (http://www.informatik.uni-trier.de/~ley/db/) 788,259 author’s names 1,226,412 publications 3,490 venues (conferences, workshops, journals) CiteSeerX (http://citeseerx.ist.psu.edu/) 7,385,652 publications (including publications in reference lists) 22,735,240 citations Over 4 million author’s names Combination Canopy clustering [ McCallum 2000 ] Result: 864,097 matched pairs On average: venues cite 2306 and are cited 2037 times](https://image.slidesharecdn.com/i-know2010pham-110725033918-phpapp01/85/Clustering-Technique-for-Collaborative-Filtering-Recommendation-and-Application-to-Venue-Recommendation-6-320.jpg)
![User-based CF: Author Clustering Data: DBLP Perform 2 test cases for the years of 2005 and 2006 Clustering of co-authorship networks 2005s network: 478,108 nodes; 1,427,196 edges 2006s network: 544,601 nodes; 1,686,867 edges Prediction of the venue participation Clustering algorithm Density-based algorithm [Clauset 2004 ] Obtained modularity: 0.829 and 0.82 Cluster size distribution follows Power law](https://image.slidesharecdn.com/i-know2010pham-110725033918-phpapp01/85/Clustering-Technique-for-Collaborative-Filtering-Recommendation-and-Application-to-Venue-Recommendation-7-320.jpg)
![Item-based CF: Venue Network Creation and Clustering Knowledge network Aggregate bibliography coupling counts at venue level Undirected graph G(V, E) , where V : venues, E : edges weighted by cosine similarity Threshold: Clustering: density-based algorithm [ Neuman 2004, Clauset 2004 ] Network visualization: force-directed paradigm [ Fruchterman 1991 ] Knowledge flow network (for venue ranking, see Pham & Klamma 2010 ) Aggregate bibliography coupling counts at venue level Threshold: citation counts >= 50 Domains from Microsoft Academic Search ( http://academic.research.microsoft.com/)](https://image.slidesharecdn.com/i-know2010pham-110725033918-phpapp01/85/Clustering-Technique-for-Collaborative-Filtering-Recommendation-and-Application-to-Venue-Recommendation-9-320.jpg)
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Clustering Technique for Collaborative Filtering Recommendation and Application to Venue Recommendation
- 1. Clustering Techniques for Collaborative Filtering and the Application to Venue Recommendation Manh Cuong Pham , Yiwei Cao, Ralf Klamma Information Systems and Database Technology RWTH Aachen, Germany Graz , Austria, September 01, 2010 I-KNOW 2010
- 2. Agenda Introduction Clustering techniques for collaborative filtering Case study: venue recommendation Data sets: DBLP and CiteSeerX User-based Item-based Conclusions and Outlook
- 3. Introduction Recommender systems: help users dealing with information overload Components of a recommender system [ Burke2002 ] Set of users, set of items (products) Implicit/explicit user rating on items Additional information: trust, collaboration, etc. Algorithms for generating recommendations Recommendation techniques [ Adomavicius and Tuzhilin 2005 ] Collaborative Filtering (CF) [Breese et al. 1998 ] Memory-based algorithms: user-based, item-based [Sarwar 2001] Model-based algorithms: Bayesian network [ Breese1998 ] ; Clustering [ Ungar 1998 ] ; Rule-based [ Sarwar2000 ] ; Machine learning on graphs [Zhou 2005, 2008]; PLSA [Hofmann 1999] ; Matrix factorization [Koren 2009] Content-based recommendation [Sarwar et al. 2001] Hybrid approaches [Burke 2002]
- 4. Clustering and Collaborative Filtering Cluster 2 Cluster 1 item-based CF User clustering Item clustering item-based CF item-based CF Problems: large-scale data; sparse rating matrix; diversity of users and items Previous approaches: Clustering based on ratings K-means, Metis, etc. [Rashid 2006, Xue 2005, O’Connor 2001] Our approach Clustering based on additional information: relationships between users, items Improvement on both efficiency and accuracy x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
- 5. Evaluation: Venue Recommendation Recommend venues (conferences, journals, workshops) to researchers User-based CF Populate user-item matrix using venue participation history Ratings: normalized venue publication counts User-clustering: co-authorship network Item-based CF Similarity between venues based on citation Similarity measure: cosine Venue clustering: similarity network
- 6. Data Sets DBLP (http://www.informatik.uni-trier.de/~ley/db/) 788,259 author’s names 1,226,412 publications 3,490 venues (conferences, workshops, journals) CiteSeerX (http://citeseerx.ist.psu.edu/) 7,385,652 publications (including publications in reference lists) 22,735,240 citations Over 4 million author’s names Combination Canopy clustering [ McCallum 2000 ] Result: 864,097 matched pairs On average: venues cite 2306 and are cited 2037 times
- 7. User-based CF: Author Clustering Data: DBLP Perform 2 test cases for the years of 2005 and 2006 Clustering of co-authorship networks 2005s network: 478,108 nodes; 1,427,196 edges 2006s network: 544,601 nodes; 1,686,867 edges Prediction of the venue participation Clustering algorithm Density-based algorithm [Clauset 2004 ] Obtained modularity: 0.829 and 0.82 Cluster size distribution follows Power law
- 8. User-based CF: Performance Precisions for 1000 random chosen authors Precisions computed at 11 standard recall levels 0%, 10%,….,100% Results Clustering performs better Not significant improved Better efficiency Further improvement Different networks: citation Overlapping clustering
- 9. Item-based CF: Venue Network Creation and Clustering Knowledge network Aggregate bibliography coupling counts at venue level Undirected graph G(V, E) , where V : venues, E : edges weighted by cosine similarity Threshold: Clustering: density-based algorithm [ Neuman 2004, Clauset 2004 ] Network visualization: force-directed paradigm [ Fruchterman 1991 ] Knowledge flow network (for venue ranking, see Pham & Klamma 2010 ) Aggregate bibliography coupling counts at venue level Threshold: citation counts >= 50 Domains from Microsoft Academic Search ( http://academic.research.microsoft.com/)
- 10. Knowledge Network: the Visualization
- 12. Interdisciplinary Venues: Top Betweenness Centrality
- 13. High Prestige Series: Top PageRank
- 14. Conclusions and Future Research Clustering and recommender systems Advantage of using additional information for clustering Application of clustering for both user-based and item-based CF Key issue: impact of the communities (cluster) on the quality of recommendations; non-overlapping communities vs. overlapping communities Outlook Further evaluation: trust networks clustering, paper and potential collaborator recommendation Datasets: Epinion, Last.fm, etc. Digital libraries in Web 2.0: Mendeley, ResearchGate, etc.
Editor's Notes
- #3: Pham Manh Cuong