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Information filtering on dynamical networks Associate Prof. Jianguo Liu

Information filtering on dynamical networks Associate Prof. Jianguo Liu University of Shanghai for Science and Technology 2010-8-13 E-mail:liujg004@gmail.com. Outline. Why recommendation systems are needed? How to recommend new information? Some proposed works. Conclusion and discussions.

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Information filtering on dynamical networks Associate Prof. Jianguo Liu

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  1. Information filtering on dynamical networks Associate Prof. Jianguo Liu University of Shanghai for Science and Technology 2010-8-13 E-mail:liujg004@gmail.com

  2. Outline • Why recommendation systems are needed? • How to recommend new information? • Some proposed works. • Conclusion and discussions

  3. Our Group • University of Shanghai for Science and Technology • Prof. Yi-Cheng Zhang, Jian-Guo Liu, Qiang Guo • University of Fribourg • Prof. Yi-Cheng Zhang, Medo Matus, Zico, Linyuan, Cihang • University of Science and Technology of China • Prof. Bing-Hong Wang • University of Electronic Science and Technology of China • Prof. Tao Zhou, Ming-Sheng Shang, Le Dong

  4. 1.Why recommend? Facebook CEO

  5. Why recommend We face too much data and sources to be able to find out those most relevant for us. Indeed, we have to make choices from thousands of movies, millions of books, billions of web pages, and so on. Evaluating all these alternatives by ourselves is not feasible at all. As a consequence, an urgent problem is how to automatically find out the relevant objects for us.

  6. 2. Recommendation algorithms • Collaborative filtering algorithm • Content-based algorithm • Struture-based algorithms

  7. 2.1Collaborative filtering algorithm Herlocker et al., ACM Trans. Inf. Syst. 22: 5-53 (2004)

  8. 2.2Content-based algorithm The user will be recommended items similar to the ones this user preferred in the past Pazzani & Billsus, LNCS 4321: 325-341 (2007)

  9. 2.3 Structure-based algorithms

  10. 3. Hybrid algorithm Heat conduction Mass diffusion T. Zhou, Z. Kuscisik, JG Liu, M. Medo, JR Wakeling, YC Zhang, PNAS 107(10) 4511 (2010) .

  11. Hybrid algorithm

  12. 3.2 Information filtering on weighted user-object bipartite networks

  13. 4. Conclusion and discussions • What’s the meaning of the edge weight possion distribution？ • How to design the efficient dynamic algorithm； • What’s the relationship between the statistical properties of the data and the recommendation performance? • How to construct the mathematical model? • The evolution model based on the link prediction mechanism.

  14. Thanks to NSFC(10905052,70901010), and Shanghai Leading Discipline Project (No. S30501).

  15. Many thanks!!

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