DEIM Forum 2013 A2-2 606 8501 E-mail: kato@dl.kuis.kyoto-u.ac.jp 1. 2. 1 4 A B C D A B C D A : B :: C : D : :: : : :: : A B C D A= B= C= D= D 3 Turney [20] A B C D A B C D Bollegala [5] Web SVM A B C D 2. 1 A, B t t C A : B :: C : D D t A B A B C D t A B T Web A B A B 1 A B A B B A Web n 2 A B Web 3 4 t i A B A B t i χ 2 B A

A B 5 α t i A B A B T T A B C D A B A B t i C 6 T t i C t i t i C Web n 7 T t i C t i Web 8 9 d j C t i C t i d j χ 2 t i C C t i P C(d j) P ti (d j) 10 β d j C t i P C(d j) P ti (d j) P C,ti (d j) 11 T t i P C,ti (d j) d j d j 2. 2 33 854 1 2 2 1 TF-IDF 2 [15] @5 @10 @20 5 10 20 MRR @5 @10 @20 D 20 49.8% [15] [11] 1 A( ) B( ) C( ) D() 2 MRR @k k MRR @5 @10 @20 1 0.045 8.3 10.8 18.7 2 0.150 24.5 30.9 33.8 0.249 34.4 42.1 49.8 3. 3. 1 Web Web 1 2 Booking.com 3 Web Web Web 1 http://www.gnavi.co.jp/ 2 http://tabelog.com/ 3 http://www.booking.com/

1 ( ) ( ) 3. 2 (Query by example) [1] (Contentbased retrieval [6], [7], [18]) 1 1 2 3. 3 2 1 2 1 [21] 2 4,000 4,000 1 2 2 2 1 MindReader [8] MindReader

Content-based search MindReader [12] 2 4. (relative aggregation point, RAP) 4. 1 X P (X) D = (X, P (X)) [16] Y D P (X) X X D = {(x 1, y 1), (x 2, y 2),...} D (S) X (S) X (S) Q X (S) D (S) = {(x (S), +1) x (S) Q} {(x (S), 1) x (S) (X (S) Q)} D (T ) X (T ) X (T ) D (S) X (T ) f : X (T ) R X (T ) x (T ) i x (T ) j f(x (T ) i ) < f(x (T ) j ) (X (T ), ) f X (S) X (T ) X (S) = {x (S) 1, x (S) 2, x (S) 3 } P (X (S) ) X (S) Q = {x (S) 1, x (S) 2 } D (S) = {(x (S) 1, +1), (x (S) 2, +1), (x (S) 3, 1)} D (S) D (T ) X (T ) = {x (T ) 1, x (T ) 2, x (T ) 3 } 1,000 (X (S) = X (T ) ) (P (X (S) ) = P (X (T ) )) A [2], [17] A D (S) = D (T ) 4. 2 RAP RAP RAP 4. 2. 1 RAP D X ψ MAX (X) = argmax x X x b x b x

Source Domain Category 3 2 1 Cost Target Domain Category 2 3 1 Cost RAP H = {h 1, h 2,... h m} A = {a 1, a 2,... a n} RAP H ϕ : X R mn x mn ϕ(x) ϕ(x) = (ϕ 1 (x), ϕ 2 (x),..., ϕ n (x)) (3) 2 RAP RAP RAP X X D ψ : 2 X 2 X (2 X X ) RAP a ψ = x X xp (x ψ)dx, (1) x x P (x ψ) D X ψ(x) x P (x ψ) P (x ψ) = P (x ψ, X)P (X)dX, (2) X 2 X P (x ψ, X) X ψ(x) x ψ P (x ψ) 2 RAP ( ) 2 1 2 3 RAP 2 (b (S) 1 = b (T ) 1 ) (c (S) 3 = c (T ) 3 ) 10,000 15,000 10,000 15,000 RAP RAP ϕ i (x) = (h 1 (x, a i ), h 2 (x, a i ),..., h m (x, a i )) RAP x x x RAP ϕ(x) (x, λϕ(x)) λ RAP x ϕ(x) L 2 RAP ϕ (S) RAP ϕ (T ) x (S) X (S) (x (S), λϕ (S) (x (S) )) x (T ) X (T ) (x (T ), λϕ (T ) (x (T ) )) ( x (S) x (T ) ) RAP ( ϕ (S) (x (S) ) ϕ (T ) (x (T ) ) ) RAP RAP [10] 4 4. 3 2 (i) (ii) RAP 4. 3. 1 D (S) D (T ) 3 4 Geographic Object Retrieval Dataset http://www.mpkato.net/ jp/datasets/

3 SVM ndcg@10 in-domain Target Kyoto Tokyo Sapporo Fukuoka Nagoya Kyoto 0.620 0.463 0.520 0.511 0.431 Source Tokyo 0.460 0.596 0.449 0.501 0.389 Sapporo 0.492 0.432 0.638 0.524 0.506 Fukuoka 0.505 0.472 0.510 0.588 0.477 Nagoya 0.476 0.466 0.538 0.527 0.599 +1 1 D (S) D (S) X (T ) X (T ) f MAP ndcg 10 ndcg 10 ndcg@10 (NNS) 1 SVM(OSVM) SVM NNS D (S) OSVM D (S) NNS OSVM SVM D (S) OSVM transductive support vector machine (TSVM) structural correspondence learning (SCL) relative cluster mapping (RCM) Joachims TSVM [13], [22] OSVM SVM 1 SCL [3], [4] SVM RCM SCL SVM [14] 4. 3. 2 SVM in-domain (D (S) = D (T ) ) k k k 1 out-domain (D (S) = D (T ) ) in-domain k k 1 1 k = 5 100 5 20 5 500 3 ndcg@10 SVM out-domain (D (S) = D (T ) ) ndcg@10 in-domain (D (S) = D (T ) ) ndcg@10 A (Pearson s coefficient r = 0.678, p < 0.05) ndcg@10 in-domain 0.608 out-domain 0.482 t in-domain out-domain (t(179) = 5.89, p < 0.001) 5 Cohen s d (0.588) (in-domain out-domain) ( ) [19] [9] 1 4. 3. 3 RAP out-domain 400 (4 100 ) 4 5 MAP ndcg@10 MAP ndcg@10 (F (6, 2793) = 16.1, p < 0.001 F (6, 2793) = 40.0, p < 0.001) t RAP 6 MAP ndcg@10 NNS OSVM TSVM RAP 5 11 19 ndcg@10 5 8,000 11 5 MAP in-domain (0.609) out-domain (0.479) (t(157) = 5.35, p < 0.001, Cohen s d = 0.585) 6 0.05 Holm

Domain-dependent Domain-independent 4 MAP Inductive Transductive Intent NNS OSVM SVM TSVM SCL RCM RAP 1 0.539 0.546 0.490 0.417 0.482 0.490 0.490 2 0.387 0.452 0.522 0.439 0.512 0.521 0.574 3 0.347 0.319 0.317 0.344 0.321 0.317 0.320 4 0.409 0.350 0.672 0.558 0.688 0.672 0.668 5 0.488 0.458 0.512 0.506 0.515 0.513 0.522 6 0.198 0.227 0.327 0.329 0.324 0.327 0.333 7 0.078 0.083 0.155 0.150 0.153 0.155 0.144 8 0.192 0.219 0.254 0.206 0.251 0.254 0.260 9 0.488 0.491 0.548 0.561 0.548 0.548 0.570 10 0.261 0.293 0.335 0.305 0.329 0.335 0.337 11 0.442 0.401 0.630 0.581 0.631 0.630 0.654 12 0.180 0.177 0.280 0.233 0.284 0.280 0.284 13 0.586 0.571 0.645 0.627 0.638 0.645 0.640 14 0.115 0.114 0.161 0.109 0.161 0.161 0.156 15 0.354 0.293 0.545 0.506 0.541 0.545 0.538 16 0.416 0.399 0.702 0.609 0.686 0.701 0.705 17 0.270 0.270 0.379 0.317 0.384 0.379 0.385 18 0.256 0.226 0.401 0.312 0.398 0.401 0.399 19 0.586 0.595 0.641 0.532 0.646 0.641 0.645 20 0.288 0.286 0.238 0.203 0.236 0.238 0.239 Total 0.344 0.338 0.438 0.392 0.436 0.438 0.443 Domain-dependent Domain-independent 5 ndcg@10 Inductive Transductive Intent NNS OSVM SVM TSVM SCL RCM RAP 1 0.191 0.192 0.175 0.176 0.171 0.177 0.173 2 0.173 0.249 0.406 0.277 0.415 0.406 0.420 3 0.149 0.123 0.084 0.151 0.103 0.084 0.086 4 0.145 0.087 0.389 0.315 0.367 0.386 0.395 5 0.182 0.167 0.242 0.250 0.260 0.242 0.283 6 0.118 0.162 0.267 0.213 0.261 0.267 0.268 7 0.024 0.046 0.174 0.121 0.170 0.174 0.168 8 0.213 0.251 0.303 0.219 0.311 0.303 0.309 9 0.199 0.184 0.254 0.223 0.258 0.254 0.282 10 0.130 0.178 0.277 0.226 0.269 0.278 0.266 11 0.208 0.172 0.589 0.582 0.617 0.589 0.631 12 0.148 0.121 0.288 0.162 0.296 0.288 0.295 13 0.201 0.198 0.295 0.262 0.283 0.295 0.325 14 0.068 0.056 0.184 0.038 0.194 0.184 0.183 15 0.285 0.230 0.483 0.513 0.509 0.483 0.492 16 0.164 0.136 0.487 0.375 0.496 0.486 0.486 17 0.107 0.135 0.418 0.230 0.475 0.418 0.440 18 0.222 0.174 0.368 0.206 0.356 0.368 0.323 19 0.305 0.345 0.492 0.378 0.493 0.492 0.540 20 0.118 0.109 0.127 0.081 0.128 0.120 0.121 Total 0.168 0.166 0.315 0.250 0.322 0.315 0.324 7 RAP 5. 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