1. © Business School, 2010 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. © Business School, 2010 Outline 1.Why recommendation systems are needed? 2.How to recommend new information? 3.Some proposed works. 4.Conclusion and discussions

3. © Business School, 2010 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. © Business School, 2010 1.Why recommend? Facebook CEO

5. © Business School, 2010 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. © Business School, 2010 2. Recommendation algorithms 1.Collaborative filtering algorithm 2.Content-based algorithm 3.Struture-based algorithms

7. © Business School, 2010 2.1Collaborative filtering algorithm Herlocker et al., ACM Trans. Inf. Syst. 22: 5-53 (2004)

8. © Business School, 2010 The user will be recommended items similar to the ones this user preferred in the past Pazzani & Billsus, LNCS 4321: 325-341 (2007) 2.2Content-based algorithm

9. © Business School, 2010 2.3 Structure-based algorithms

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

11. © Business School, 2010 Hybrid algorithm

12. © Business School, 2010 3.2 Information filtering on weighted user- object bipartite networks

13. © Business School, 2010

15. 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.

16. © Business School, 2010 Thanks to NSFC(10905052,70901010), and Shanghai Leading Discipline Project (No. S30501).

17. © Business School, 2010 Many thanks!!