DEIM Forum 2009 C8-4 QA NTT QA QA QA 2 QA Abstract Questions Recomme

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1 DEIM Forum 2009 C8-4 QA NTT {kabutoya.yutaka,kawashima.harumi,fujimura.ko}@lab.ntt.co.jp QA QA QA 2 QA Abstract Questions Recommendation Based on Evolution Patterns of a QA Community Yutaka KABUTOYA, Harumi KAWASHIMA, and Ko FUJIMURA NTT Cyber Solutions Laboratories, NTT Corporation Hikarinooka 1 1, Yokosuka-shi, Kanagawa, Japan {kabutoya.yutaka,kawashima.harumi,fujimura.ko}@lab.ntt.co.jp In an existing QA site, it is necessary for repliers to retrieve attractive questions for themselves. In this paper, we propose a method to retrieve questions for which each replier is likely to give an answer. In our method, based on the possibility of a new answer in local structure of the QA network, we discover two users between whom a new answer is likely to be derived. The QA Network is such a graph that each node mean a user and each edge mean an answer. In this paper, we describe our method and report experimental evaluation of our method. Key words question answering, network motifs, time series analysis, link prediction 1. QA Web u QA v 2 QA QA goo 1 Yahoo! u v goo 400 QA QA QA u QA v v ( ) u QA 1 u v QA 3 t 0 t 1 t 0

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6 3 @1000 : PHP ( ) Java χ @ QA PHP [1] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network Motifs: Simple Building Blocks of Complex Networks. Science, Vol. 298, No. ( ) , pp , [2] S. Wernicke. Efficient Detection of Network Motifs. Java IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, pp , [3],, , pp , [4] MEJ Newman. Clustering and preferential attachment in 5. 5 growing networks. Physical Review E, Vol. 64, No. 2, p , [5] R. Baeza-Yates, B. Ribeiro-Neto, et al. Modern information 2 (u, v) retrieval. Addison-Wesley Harlow, England, v u [6] D. Liben-Nowell and J. Kleinberg. The Link-Prediction Problem for Social Networks. JOURNAL-AMERICAN SO- CIETY FOR INFORMATION SCIENCE AND TECH- 2 (u, v) v NOLOGY, Vol. 58, No. 7, p. 1019, u v [7] L.A. Adamic and E. Adar. Friends and neighbors on the Web. Social Networks, Vol. 25, No. 3, pp , [8] L. Katz. A new status index derived from sociometric analysis. Psychometrika, Vol. 18, No. 1, pp , [9] AL Barabási, H. Jeong, Z. Néda, E. Ravasz, A. Schubert, 6. and T. Vicsek. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, Vol. 311, No. 3-4, pp , [10],,. QA.., Vol. 2008, No. 88, pp , [11] L.A. Adamic, J. Zhang, E. Bakshy, and M.S. Ackerman. Knowledge sharing and Yahoo Answers: Everyone knows something. Proceedings of the 17th international conference on World Wide Web, [12],,,.., pp. 1 10, goo 10 [13],,. Yahoo!., pp ,

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Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy

Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy 1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura