About how user interests (more specifically research interests of scientists) can be quantitatively analized and used in personalized Web search (Invited talk at Microsoft Research Asia NLC Group).

1. Research Interests : Their Dynamics, Structures and Applications in Personalized Web Search Yi Zeng 1 , Erzhong Zhou 1 , Xu Ren 1 , Yulin Qin 1,3 , Ning Zhong 1,2 , Zhisheng Huang 4 1. International WIC Institute, Beijing University of Technology, China 2. Maebashi Institute of Technology, Japan 3. Carnegie Mellon University, USA 4. Vrije University Amsterdam, the Netherlands

2. Web Intelligence Consortium

11. Building and Analyzing the Structure of Research Interests Observed Phenomenon: [1] main research interests ( pivotal nodes ) are dynamically changing all the time. With older ones disappear and new ones emerged . [2] Relations among research interests varies as time passed ( strengthen or weaken ). [3] main research interests are closely related to each other. (The closeness is getting stronger from time to time, which made the degree of separation around 2-3. It indicates that for an author, research interests are not isolated but highly relevant . [4] Many top research interests (pivotal nodes) remain active in the interest network (e.g. search, analysis, match). Figure 7 . Ricardos research interest dynamic evolution network from 1991 to 2009. (Based on DBLP publication list, with 232 papers involved). The network is a graph with weighted edges and weighted vertices . An Author’s Research Interest Evolution Network

16. A Comparative Study of Different Interest Evaluation Methods Interests Longest Duration Interests Cumulative Duration Zhisheng Huang’s Interests Evaluation from CI, ILD and ICD

18. Overlap of User Interests and Group Interests Top 9 interests retention of a user and his group interests retention. (Ricardo A. Baeza-Yates, based on May 2008 version of SwetoDBLP). … Model … Analysis … Text … Challenge 14 Analysis 1.26 Minining 18 Query 2.10 Engine 19 System 2.14 Query 26 Information 2.27 Information 28 Web 3.19 Retrieval 30 Retrieval 5.59 Search 35 Search 7.81 Web Top 9 Group Retained Interests Top 9 Retained Interests

20. Semantic Similarity and Interests Re-ranking Semantic Similarity judges by Normalized Google Distance [Rudi and Paul 2007] Normalized Google Distance Google, Bing as the Knowledge base. 0.080 reasoning ontology 0.460 pagerank Query 0.332 ontology logic 0.497 pagerank retrieval 0.050 semantic reasoning 0.403 query retrieval -0.003 semantic ontology 0.490 pagerank search 0.276 semantic logic 0.483 query search 0.239 reasoning logic 0.529 retrieval search NGD interest y interest x NGD interest y interest x

23. Evaluations on Normalized Medline Distance (NMD) Experts evaluated 30 medline term pairs Pearson Correlation: NMD gets the highest value among the measures, 0.792 T-test significance: 0.995 Experts from AstraZeneca evaluated 90 randomly generated pairs Pearson Correlation: NMD: 0.736 vs NGD:0.531 Average: Experts:0.590, NMD:0.390, NGD:0.289 NMD is closer to experts’ evaluation

27. Integration of WI and e-FOAF:interests by FOAF community By Balthasar A.C. Schopman from Vrije University Amsterdam

33. DBLP-SSE : DBLP Search Support Engine The DBLP dataset Web Semantic Knowledge Sub datasets pre-selection * Web Intelligence and Artificial Intelligence in Education. * Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-A New Standard for System Diagnostics. * Semantic Model for Artificial Intelligence Based on Molecular Computing . * Open Information Systems Semantics for Distributed Artificial Intelligence . * Artificial Intelligence and Financial Services . * … with current interests constraints (Top 5 results) List 2 : * PROLOG Programming for Artificial Intelligence , Second Edition. * Artificial Intelligence Architectures for Composition and Performance Environment. * Artificial Intelligence in Music Education: A Critical Review. * Music, Intelligence and Artificiality. Artificial Intelligence and Music Education. * Musical Knowledge: What can Artificial Intelligence Bring to the Musician? * ... without current interests constraints (Top 5 results) List 1 : Artificial Intelligence Query : Web, Service, Semantic, Architecture, Model, Ontology, Knowledge, Computing, Language Top 9 interests Dieter Fensel Log in

34. Search Results without any Refinement

35. Search Results with Interests-based Refinement http://www.wici-lab.org/wici/dblp-sse/

37. Scalability for Query Time With selection: approximately 80% of the time can be saved. equivalent to Refined query based on interests much closer to user needs may be very far from user needs Results the fastest much slower medium Query Time Interest based selection before querying Refined query based on interests Unrefined query

38. The Effect of Query Constraints Numbers

42. Thank you! URL: http://www.wici-lab.org/wici/~yizeng Email: yizeng@bjut.edu.cn