DEIM Forum 2012 D2-1 606 8501 150 0002 2-15-1 28F E-mail: {tsukuda,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp, {miyamamoto,hiwasaki}@d-itlab.co.jp 1 Wikipedia Wikipedia HITS 1. Web Web Web 1 3 Wikipedia 2 Web 2 1 2 Web Wikipedia ( ) ( ) Web ( ) Web 2. Web

[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 1 1 4 2 3 2

2 5 3 6 4 4 5 6 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

t Wikipedia Wikipedia 2 1 t Web Wikipedia 2 t QA Web Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia 4. 2 3 ALAGIN 1 Wikipedia 45 45 1 4. 3 t e i E t t e i e i t e i t e e 1http://nlpwww.nict.go.jp/corpus/ 2 2 3 2 2 HITS [13] 2 4. 3. 1 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 4. 3. 2 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)

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 ) 4. 3. 3 1 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 4.3.2 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 4. 3. 4 2 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 2 4.3.3 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/

1 HITS SALSA HITS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 d = 0.15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 1 6. 6. 1 4.3.2 HITS 1 32 9843 HITS 10 22 23 2 1 1 1 1 1 1 6 7 8 9 10 3 2 1 2 3 4 5 6 7 8 9 10 100 4.3.3 4.3.4 2 3 433 2 5 2 3 3 4.4 4.5 4 6. 2 Web 4 3 5 Web 1 Web 100 5 3

4 1 2 3 4 5 6 7 8 9 10 5 6 5 2 5 k = 100 r = 5 1 1 3 2 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. 265 270 (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. 144 153 (2001). [4] B. Padmanabhan and A. Tuzhilin: Unexpectedness as a measure of interestingness in knowledge discovery, Decis. Support Syst., 27, pp. 303 318 (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. 828 834 (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. 275 281 (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. 54 63 (2000). [8] B. Padmanabhan and A. Tuzhilin: A belief-driven method for discovering unexpected patterns, KDD, pp. 94 100 (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. 393 408 (2001). [10] B. Sarwar, G. Karypis, J. Konstan and J. Reidl: Itembased collaborative filtering recommendation algorithms,

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