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1 DEIM Forum 2013 A 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

2 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 TF-IDF @ @20 D % [15] [11] 1 A( ) B( ) C( ) D() 2 @ Web Web 1 2 Booking.com 3 Web Web Web

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

4 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 RAP D X ψ MAX (X) = argmax x X x b x b x

5 Source Domain Category Cost Target Domain Category 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 ( ) 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] (i) (ii) RAP D (S) D (T ) 3 4 Geographic Object Retrieval Dataset jp/datasets/

6 3 SVM in-domain Target Kyoto Tokyo Sapporo Fukuoka Nagoya Kyoto Source Tokyo Sapporo Fukuoka Nagoya 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] SVM in-domain (D (S) = D (T ) ) k k k 1 out-domain (D (S) = D (T ) ) in-domain k k 1 1 k = 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 out-domain t in-domain out-domain (t(179) = 5.89, p < 0.001) 5 Cohen s d (0.588) (in-domain out-domain) ( ) [19] [9] RAP out-domain 400 (4 100 ) 4 5 MAP ndcg@10 MAP ndcg@10 (F (6, 2793) = 16.1, p < F (6, 2793) = 40.0, p < 0.001) t RAP 6 MAP ndcg@10 NNS OSVM TSVM RAP ndcg@10 5 8, MAP in-domain (0.609) out-domain (0.479) (t(157) = 5.35, p < 0.001, Cohen s d = 0.585) Holm

7 Domain-dependent Domain-independent 4 MAP Inductive Transductive Intent NNS OSVM SVM TSVM SCL RCM RAP Total Domain-dependent Domain-independent 5 ndcg@10 Inductive Transductive Intent NNS OSVM SVM TSVM SCL RCM RAP Total RAP 5. [1] D. Angluin and C. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3): , [2] S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Proc. of NIPS, pp , [3] J. Blitzer, M. Dredze, and F. Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In Proc. of ACL, pp , [4] J. Blitzer, R. McDonald, and F. Pereira. Domain adaptation with structural correspondence learning. In Proc. of EMNLP, pp , [5] D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring the similarity between implicit semantic relations from the web. In Proc. of WWW 2009, pp , [6] M. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney. Content-based music information retrieval: current directions and future challenges. Proceedings of the 7 IEEE, 96(4): , [7] V. Gudivada and V. Raghavan. Content based image retrieval systems. Computer, 28(9):18 22, [8] Y. Ishikawa, R. Subramanya, and C. Faloutsos. Mindreader: Querying databases through multiple examples. In Proc. of VLDB 1998, pp , [9] M. Kamvar, M. Kellar, R. Patel, and Y. Xu. Computers and iphones and mobile phones, oh my!: a logs-based comparison of search users on different devices. In Proc. of WWW, pp , [10] M. P. Kato, H. Ohshima, and K. Tanaka. Content-based retrieval for heterogeneous domains: domain adaptation by relative aggregation points. In Proc. of SIGIR 2012, pp , [11] M. Kato, H. Ohshima, S. Oyama, and K. Tanaka. Query by analogical example: relational search using web search engine indices. In Proc. of CIKM 2009, pp , [12] M. Kato, H. Ohshima, S. Oyama, and K. Tanaka. Search as if you were in your home town: geographic search by regional context and dynamic feature-space selection. In Proc. of CIKM 2010, pp , [13] X. Ling, W. Dai, G.-R. Xue, Q. Yang, and Y. Yu. Spectral domain-transfer learning. In Proc. of KDD, pp , [14] S. Nakajima and K. Tanaka. Relative queries and the relative cluster-mapping method. In Proc. of DASFAA 2004, pp , [15] H. Ohshima and K. Tanaka. High-speed Detection of Ontological Knowledge and Bi-directional Lexico-Syntactic Patterns from the Web. Journal of Software, 5(2): , [16] S. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10): , [17] P. Rai, A. Saha, H. Daumé III, and S. Venkatasubramanian. Domain adaptation meets active learning. In Proc. of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing, pp , [18] A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on pattern analysis and machine intelligence, 22(12): , [19] J. Teevan, S. Dumais, and E. Horvitz. Potential for personalization. ACM Transactions on Computer-Human Interaction, 17(1):1 31, [20] P. Turney and M. Littman. Corpus-based learning of analogies and semantic relations. Machine Learning Journal,

8 60(1-3): , [21] A. Tversky. Features of similarity. Psychological Review, 84(4): , [22] G. Xue, W. Dai, Q. Yang, and Y. Yu. Topic-bridged plsa for cross-domain text classification. In Proc. of SIGIR, pp , 2008.

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