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1 Vol. 47 No. 4 Apr y1 y2;y3 y1 y1 y1 y4;y1 - - SVM User Modeling based on Location History YUTAKA MATSUO, y1 NAOAKI OKAZAKI, y2;y3 YOSHIYUKI NAKAMURA, y1 TAKUICHI NISHIMURA, y1 KÔITI HASIDA y1 and HIDEYUKI NAKASHIMA y1 Recent development for location detection techniques enables us to obtain location histories for users in a ubiquitous environment. This paper describes a new method to infer user attributes from a user s location history. Using the number of counts each sensor detects each user, we can obtain a user-sensor matrix, which is similar to document-term matrix in the context of information retrieval. The problem to detect a user s attributes can be reduced into text classification problem, to which support vector machine can be effectively applied. We also propose a method to measure the importance of sensors. 1. 1)ο3) 4) Active Badge 5) Active Badge GPS RFID y1 National Institute of Advanced Industrial Science and Technology y2 University of Tokyo y3 University of Manchester y4 Future University, Hakodate 6) 7) 6),8) 9),10) 11)ο13) Heckmann 13) A user model is a knowledge source in a system which containes explicit assumptions on all aspects of the user that may be relevant to the behavior of the system. These assumptions must separable by the system from the rest of the systems s knowledge. 1

2 2 Apr ) Heckmann attribute-value 2004 ffl ffl ffl Workshop on User Modeling for Ubiquitous Computing (2003) Workshop on Personalized Context Modeling and Management for UbiComp Applications(2005) UbiqUM2006(Workshop on Ubiquitous User Modeling) 2005 Journal of User Modeling and User-Adapted Interaction User Modeling in Ubiquitous Computing ),15) Kobsa 11)

3 Vol. 47 No. 4 3 Wilson 7),16) Narrotor 17) GPS location Hightower GPS WiFi LAN) GSM1 18) CoBIT ID CoBIT 19),20) CoBIT ID ID CoBIT 1 3-5m ID 2 ID CoBIT 3 ID CoBIT ),22) 3 ID ID IC RDIF GPS 17) RFID F CoBIT m CoBIT 47 23) ID

4 4 Apr Sensor ID User ID Date/Time :24: :25: :25: :01: CoBIT

5 Vol. 47 No ID - - W n m n m W ij s j u i 4 3 W = C A (1) f g , 0, 1 u1 u2 u3 s1 s2 s3 s4 coffee u1 u3 s1 s4 coffee s1 s4 coffee 1 0 u4 coffee 1 0 s1 s2 s3 s4 coffee u ? s2 s2 coffee 1 u4 coffee 1 - W n m - 24) Support Vector Machine (SVM) SVM 25) RBF radius basis function SVM 26) 27) tf idf tf idf

6 6 Apr , 24-29, 30-34, 35-39,40 SC,,, SC A, B, C, D,,,,,,, A, B, C, D, E, F A, B 2 u i s j tfidf (s j;u i)=freq(sj;u i) idf (sj ) (2) freq(s j;u i) sj u i idf (s j ) idf (s j ) = log(n=uf (sj )) (3) n uf (s j ) sj uf (s j) idf (s j ) 8 u i s j a ij ffl a ij = freq(sj;u i) ffl a ij = ( 1 if freq(s j;u i) thre 0 otherwise thre 1 ffl IDF ( idf (s j) if freq(sj;u i) thre a ij = 0 otherwise ffl ffl TFIDF a ij = tfidf (sj;u i) 1 m a P ij m (4) i=1 aij a normalized ij = F (%) Recall(%) Precision(%) TFIDF IDF IDF TFIDF SVM 2 SVM Leave-one-out RBF 2 8 Recall Precision F F Recall Precision F = 2Recall Precision Recall + Precision Recall % Precision % Recall 70% Precision 50% F

7 Vol. 47 No F F (BL) ( ) (32.95) age0* (25.53) 25 age (25.53) age (45.28) age (29.17) age4* (39.22) 40 ( ) (32.66) position (20.00) SC position (44.83) position2* (42.11) position (36.36) position4* (20.00) ( ) (28.50) team0* (34.04) A team1* (30.43) B team (22.73) C team3* (26.67) D team4* (26.67) team5* (30.43) ( ) (49.42) atd (60.00) atd (51.06) atd2* (37.21) ( ) (48.90) coffee (56.41) coffee (60.00) coffee2* (30.30) ( ) (57.58) smoking (93.33) smoking1* (22.22) ( ) (28.29) room0* (23.08) B room1* (26.42) C room2* (19.61) D room3* (29.63) A room4* (29.63) E room5* (41.38) F (63.74) station (83.72) A station (43.75) B (38.38) F TFIDF F 3 F F smoking0 F BLF 10 * F 60 F A B) 1 F

8 8 Apr F CoBIT 70% E SVM P n ffl freq w(s j)= j=1 freq(sj;ui) ffl user-freq w(s j)=uf (sj ) ffl ffl P n TFIDF tfidf w(s j)= j=1 tfidf (sj;ui) 3 nornalized (4) nx w(s j )= j=1 a normalized ij ffl wfreq nx w(s j )= freq(s j;u i) log(m=sf (ui)) j=1 sf (u i) ui TFIDF 7

9 Vol. 47 No. 4 9 F value wfreq tfidf freq 30 user-freq tfidf(normalized) 25 freq(normalized) user-freq(normalized) random Number of enabled sensors F SVM 3 * 18 * F 18 28),29) F F (random) F (wfreq) (freq (user-freq) TFIDF (tfidf) A-B-C A-C A-B-C - ffl ffl bag of words ffl ffl ffl

10 10 Apr SVM tf idf 1) (2002). 2) (2004). 3) Vol.18, No.4, pp (2001). 4) Hightower, J. and Borriello, G.: Location Systems for Ubiquitous Computing, IEEE Computer, Vol.34, No.8, pp (2001). 5) Want, R., Hopper, A., Falcao, V. and Gibbons, J.: The Active Badge Location System, ACM Transactions on Information Systems, Vol.10, No.1, pp (1992). 6) Vol.20, No.5, pp (2005). 7) Wilson, D. H.: The Narrator : A Daily Activity Summarizer Using Simple Sensors in an Instrumented Environment, Proc. UbiComp 2003 (2003). 8) Vol.46, No.12, pp (2005). 9) (2005). 10) -CONSORTS Vol.47, No.2, pp (2006). 11) Kobsa, A.: Generic User Modeling Systems, User Modeling and User-Adapted Interaction, Vol.11, pp (2001). 12) Brusilovsky, P.: Methods and techniques of adaptive hypermedia, User Modeling and User-Adapted Interaction, Vol.6, pp (1996).

11 Vol. 47 No ) Heckmann, D.: Ubiquitous Use Modeling, Ph.d thesis, University of Saarland (2005). 14) No.2003-UBI-002, pp (2003). 15) (2001). 16) Wilson, D., Long, A. and Atkeson, C.: A ContextAware Recognition Survey for Data Collection Using Ubiquitous Sensors in the Home, Proc. CHI 2005 (2005). 17) Ashbrook, D. and Starner, T.: Using GPS to learn significant locations and predict movement across multiple users, Personal and Ubiquitous Computing, Vol.7, No.5, pp (2003). 18) Hightower, J., Consolvo, S., LaMarca, A., Smith, I. and Hughes, J.: Learning and Recognizing the Places We Go, Proc. UbiComp 2005 (2005). 19) CHOBIT Vol.44, No.11, pp (2003). 20) Nakamura, Y., Nishimura, T., Itoh, H. and Nakashima, H.: ID-CoBIT: A Battery-less Information Terminal with Data Upload Capability, Proc. IECON 2003 (2003). 21) 2003 Vol.19, No.1, pp (2004). 22) Nishimura, T., Nakamura, Y., Itoh, H. and Nakamura, H.: System Design of Event Space Information Support Utilizing CoBITs, Proc. ICDCS 2004, pp (2004). 23) Schulz, D., Fox, D. and Hightower, J.: People Tracking with Anonymous and ID-Sensors Using Rao-Blackwellised Particle Filters, Proc. IJCAI-03, pp (2003). 24) Manning, C. and Schütze, H.: Foundations of statistical natural language processing, The MIT Press, London (2002). 25) Vapnik, V.: The Nature of Statistical Learning Theory, Springer-Verlag (1995). 26) ( ) (2005). 27) - - Vol.42, No.7, pp (2001). 28) Joachims, T.: Text categorization with support vector machines, Proc. ECML 98, pp (1998). 29) Mladenic, D., Brank, J., Grobelnik, M. and Milic- Frayling, N.: Feature selection using linear classifier weights: interaction with classification models, Proc. SIGIR 2004, pp (2004). ( ) ( ) Web AAAI INSNA (National Centre for Text Mining) IEEE

12 12 Apr () NKK( ) X 1995 RWCP 1998 NKK( ) 1999 RWCP ( ) ( ) ACM

B HNS 7)8) HNS ( ( ) 7)8) (SOA) HNS HNS 4) HNS ( ) ( ) 1 TV power, channel, volume power true( ON) false( OFF) boolean channel volume int

B HNS 7)8) HNS ( ( ) 7)8) (SOA) HNS HNS 4) HNS ( ) ( ) 1 TV power, channel, volume power true( ON) false( OFF) boolean channel volume int SOA 1 1 1 1 (HNS) HNS SOA SOA 3 3 A Service-Oriented Platform for Feature Interaction Detection and Resolution in Home Network System Yuhei Yoshimura, 1 Takuya Inada Hiroshi Igaki 1, 1 and Masahide Nakamura