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1 DEIM Forum 2012 D F 1 Wikipedia Wikipedia HITS 1. Web Web Web 1 3 Wikipedia 2 Web Web Wikipedia ( ) ( ) Web ( ) Web 2. Web

2 [1] Wikipedia Wikipedia [2] SNS Liu [3] 2 Web Web Web Liu Web Web [4] [5] [6] [7] [8] Web [9] Sarwar [10] Kamahara [11] [12] 3. Web

3 t e t e Rel(t, e) e Cog(e) f : Unexp(t, e) = f(rel(t, e), Cog(e)) (1) t e 4. 1 t t E t = {e 1, e 2, e n } 2 t 3 t Rel(t, e i ) 4 Cog(e i ) 5 Unexp(t, e i ) 4. 1 t t Web t t Web QA t

4 t Wikipedia Wikipedia 2 1 t Web Wikipedia 2 t QA Web Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia ALAGIN 1 Wikipedia t e i E t t e i e i t e i t e e 1http://nlpwww.nict.go.jp/corpus/ HITS [13] q t hyper(t) t hypo(t) t rel(t) Q {q} H q {x x hyper(q)} C q {x x hypo(y), y H q} L q {x x rel(q)} H lq {x x hyper(y), y L q} L c {x x rel(y), y C q, x / L q} 2 (n 1, n 2) n 1 n 2 (q, x) where x H q (x, y) where x H q, y C q, and y = hypo(x) (x, y) where x C q, y L c, and y = rel(x) (x, y) where x C q, y L q, and y = rel(x) (x, y) where x L c, y H lq, and y = hyper(x) (x, y) where x H lq, y L q, and x = hyper(y) q x L q q 2 G 1 = (H q T, E 1) T = Q C q E 1 H q T h i H q t j T h i t j 2 HITS C q q h i x i t j y j x i y j x i = t j T w th ji y j (2)

5 y j = h i H q w ht ij x i (3) wji th wij ht wji th t j h i HITS 1 2 HITS SALSA [14] SALSA h i w ht ij = 1 hypo(h i ) C q L q L c 2 G 2 = (C q L, E 2 ) L = L q L c E 2 C q L Wikipedia c i C q l j L c i l j 2 HITS c i C q SALSA C q Co-HITS [15] x 0 i c i y 0 j l j c i x i l j y j x i y j x i = (1 λ c )x 0 i + λ c y j = (1 λ l )y 0 j + λ l l j L λ c [0.1] λ l [0.1] x 0 i y 0 j w lc jiy j (4) wijx cl i (5) c i C q x 0 i y 0 j L q L c H lc 2 G 3 = (L H lc, E 3) E 3 L H lc l i L h j H lc l i h j Co-HITS SALSA 3 e i t e i Rel(t, e i) 4. 4 Wikipedia PageRank [16] Yahoo! API 2 Web e i Web Cog(e i) 4. 5 Unexp(t, e i) Unexp(t, e i) = f(rel(t, e i), Cog(e i)) 1 = log10cog(ei) (6) Rel(t, e i) 5. Web Yahoo! API Web k LexRank [17] LexRank MeCab 3 tf idf p LexRank p p = [du + (1 d)b] T p U 1 k B k B ij, i j 1 k d dampingfactor 2http://developer.yahoo.co.jp/webapi/search/websearch/v1/websearch.html 3http://mecab.sourceforge.net/

6 1 HITS SALSA HITS d = LexRank MMR [18] MMR MMR = argmax[λ(score(d i)) (1 λ) max Sim(di, dj)] d i D\S d j S D Score(d i ) LexRank d i S D Sim(d i, d j ) d i d j λ [0, 1] λ = 0.5 MMR r Web HITS HITS Web Web 1 Web

7 k = 100 r = Web 2 26 Wikipedia Wikipedia Web 7. Web COE. [1] Y. Noda, Y. Kiyota and H. Nakagawa: Proc. of 4th Int l AAAI Conference on Weblogs and Social Media, ICWSM 10. [2],,,,., 2007, 65, pp (2007). [3] B. Liu, Y. Ma and P. S. Yu: Discovering unexpected information from your competitors web sites, Proc. of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 01, pp (2001). [4] B. Padmanabhan and A. Tuzhilin: Unexpectedness as a measure of interestingness in knowledge discovery, Decis. Support Syst., 27, pp (1999). [5] B. Liu and W. Hsu: Post-analysis of learned rules, Proc. of the thirteenth national conference on Artificial intelligence - Volume 1, AAAI 96, pp (1996). [6] A. Tuzhilin: On subjective measures of interestingness in knowledge discovery, Proc. of the First International Conference on Knowledge Discovery and Data Mining, pp (1995). [7] B. Padmanabhan and A. Tuzhilin: Small is beautiful: discovering the minimal set of unexpected patterns, Proc. of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 00, pp (2000). [8] B. Padmanabhan and A. Tuzhilin: A belief-driven method for discovering unexpected patterns, KDD, pp (1998). [9] K. Swearingen and R. Sinha: Beyond algorithms: An hci perspective on recommender systems, Proc. of the 24th international ACM SIGIR conference on Research and development in information retrieval, SIGIR 01, pp (2001). [10] B. Sarwar, G. Karypis, J. Konstan and J. Reidl: Itembased collaborative filtering recommendation algorithms,

8 5 54 () W H Proc. of the 10th international conference on World Wide Web, WWW 01, pp (2001). [11] J. Kamahara, T. Asakawa, S. Shimojo and H. Miyahara: A community-based recommendation system to reveal unexpected interests, Proc. of the 11th International Multimedia Modelling Conference, MMM 05, pp (2005). [12], ( 21 ), 2007 (2007). [13] J. M. Kleinberg: Authoritative sources in a hyperlinked environment, J. ACM, 46, pp (1999). [14] R. Lempel and S. Moran: Salsa: the stochastic approach for link-structure analysis, ACM Trans. Inf. Syst., 19, pp (2001). [15] H. Deng, M. R. Lyu and I. King: A generalized co-hits algorithm and its application to bipartite graphs, Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 09, ACM, pp (2009). [16] S. Brin and L. Page: The anatomy of a large-scale hypertextual web search engine, Proc. of the seventh international conference on World Wide Web 7, WWW7, pp (1998). [17] G. Erkan and D. R. Radev: Lexrank: graph-based lexical centrality as salience in text summarization, J. Artif. Int. Res., 22, pp (2004). [18] J. Carbonell and J. Goldstein: The use of mmr, diversitybased reranking for reordering documents and producing summaries, Proc. of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 98, pp (1998).

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