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Collaborative Filtering

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Tayfun Sen

Collaborative Filtering, Web 2.0 and developments regarding the new media. www.ceng.metu.edu.tr/~e1347939/

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Collaborative Filtering ,[object Object],[object Object],You can reach the author at: stayfun{at}metu.edu.tr
What is the problem? ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
Time Person of the Year 2006: You? Yes, you. You control the Information Age. Welcome to your world.
From Time (25 Dec. 2006 edition) “It's a story about community and collaboration on a scale never seen before. It's about the cosmic compendium of knowledge Wikipedia and the million-channel people's network YouTube and the online metropolis MySpace. It's about the many wresting power from the few and helping one another for nothing and how that will not only change the world, but also change the way the world changes.”
Futurism ,[object Object],[object Object],[object Object],[object Object],[object Object]
Implications of the Recommendation in the Internet ,[object Object],[object Object],[object Object]
Examples on the Internet ,[object Object],[object Object],[object Object],[object Object]
Content based algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Collaborative Filtering algorithms ,[object Object],[object Object],[object Object],[object Object]
CF Example ,[object Object],[object Object]
CF Example (continued) ,[object Object],[object Object],[object Object]
CF Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Model based CF ,[object Object],[object Object],[object Object]
Bayesian Networks
Memory based CF ,[object Object],[object Object],[object Object]
Neighborhood based algorithms ,[object Object],[object Object]
Neighborhood based algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Neighborhood based algorithms ,[object Object],[object Object]
Cluster based algorithms ,[object Object],[object Object],[object Object],[object Object]
Cluster based algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Cluster based algorithms ,[object Object],[object Object],[object Object]
CF Metrics ,[object Object],[object Object],[object Object],[object Object],[object Object]
CF Metrics ,[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object]
References [1] May 2001 issue of the Scientific American: http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21 [2] For more information about the Web 2.0, see the wikipedia article at: http://en.wikipedia.org/wiki/Web_2.0 [3] Jon Herlocker, Joseph Konstan, John Riedl. An empirical analysis of design choices in neighborhood-based algorithms. Information Retrieval , 2002. [4] Jon Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. An algorithmic framework for performing collaborative filtering. SIGIR'99. [5] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time CF algorithm. [6] Al Manumur Rashid, Shyong K. Lam, George Karypis, and John Riedl. ClustKNN: A highly scalable hybrid model & Memory based algorithm. WEBKDD'06, 2006. [7] A. K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: a review. ACM Computing Survey 1999.

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Collaborative Filtering

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  • 4. Time Person of the Year 2006: You? Yes, you. You control the Information Age. Welcome to your world.
  • 5. From Time (25 Dec. 2006 edition) “It's a story about community and collaboration on a scale never seen before. It's about the cosmic compendium of knowledge Wikipedia and the million-channel people's network YouTube and the online metropolis MySpace. It's about the many wresting power from the few and helping one another for nothing and how that will not only change the world, but also change the way the world changes.”
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  • 26. References [1] May 2001 issue of the Scientific American: http://www.sciam.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21 [2] For more information about the Web 2.0, see the wikipedia article at: http://en.wikipedia.org/wiki/Web_2.0 [3] Jon Herlocker, Joseph Konstan, John Riedl. An empirical analysis of design choices in neighborhood-based algorithms. Information Retrieval , 2002. [4] Jon Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl. An algorithmic framework for performing collaborative filtering. SIGIR'99. [5] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time CF algorithm. [6] Al Manumur Rashid, Shyong K. Lam, George Karypis, and John Riedl. ClustKNN: A highly scalable hybrid model & Memory based algorithm. WEBKDD'06, 2006. [7] A. K. Jain, M. N. Murty, P. J. Flynn. Data Clustering: a review. ACM Computing Survey 1999.