A Bayesian Approach for Personalized Booth Recommendation

Transcription

1 2011 Inernaional Conference on Social Science and Humaniy IPED vol. (2011) (2011) IACSI Press, Singapore A Bayesian Approach for Personalized Booh ecommendaion Ki Mok Ha 2bcreaor@khu.ac.kr Il Young Choi choice102@khu.ac.kr Hyea Kyeong Kim kimhk@khu.ac.kr Jae Kyeong Kim jaek@khu.ac.kr Absrac he applicaions of new informaion and communicaion echnologies in he exhibiion indusry provide los of opporuniies for improving value of exhibiion service. Especially exhibi organizers can help he visiors o find he informaion hey are looking for hrough a recommender sysem using he ubiquious echnologies. However, exising recommender sysems in he ubiquious exhibiion environmen can reflec a visior s dynamic preference since hose sysems uilize informaion of he pre-inpued exhibi boohs. herefore, we sugges a recommendaion mehodology for our guidance modeled by a Bayesian nework in he ubiquious exhibiion environmen. A Bayesian nework can reflec a visior s dynamic preference on he exhibi boohs over ime. We expec ha he proposed mehodology will be sable and accurae o idenify he visior s dynamic preference and o recommend he exhibi booh. Keywords-componen; ecommender sysem; Bayesian nework; Exhibi booh recommendaion I. INODCION An exhibiion (i.e., rade fairs), which is defined as displaying exhibiors producs o visiors and he press [2][8], conribues o he economy in many counries. In addiion, he exhibiion indusry plays an imporan role as he effecive sales and markeing ools [13]. However, he exhibiion indusry recenly sands a he crossroads of change by he adven of new informaion and communicaion echnologies. hus exhibiion organizers have o coninuously seek and adop new echnologies o generae value for heir shows. he inroducion of ubiquious echnologies in he exhibiion indusry enables exhibiion organizers o have a lo of opporuniies for improving value of exhibiion service. he exhibiion organizers can help he visiors o find he informaion hey are looking for and guide hem o he exhibi boohs hey wan o see by adopion of he ubiquious echnology. However, he exising recommender sysems in he ubiquious exhibiion environmen can reflec a visior s dynamic preference since hose sysems uilize informaion of he pre-inpued exhibi booh. As a soluion o such a problem, we sugges a recommendaion mehodology based on a Bayesian nework for our guidance in he ubiquious exhibiion environmen. Firs, a Bayesian nework is used o reflec a visior s dynamic preference on he exhibi booh over ime because a Bayesian nework provides causal influence. Second, conex informaion is used o recommend a suiable exhibi booh o a visior s preference a he proper ime. In his sudy, locaion is used as conex informaion. II. ELAED WOK A. ecommender sysem in he exhibiion environmen As recommendaion service in he exhibiion environmen, one may refer o service provided for he personalized our guidance or exhibiion. Mos of he recommender sysems focus on building our guidance a exhibiion hall [1] [14] [10][12]. hose sysems use conex informaion for recommending proper our guidance o a visior s preference a he proper ime. For example, CyberGuide [1], which is a mobile conex-aware our guide, provides visiors wih roue and direcion based on heir locaion and orienaion. C-Map [14], which is he conexaware mobile assisan projec, provides visiors wih our guidance based on locaion and individual ineress a exhibiions. mexpess [10], which is a par of a Europeanfunded projec for supporing and faciliaing he professional exhibiion indusry in a conex-aware manner, offers a navigaion plan based on visiors locaion. And Wireless Exhibiion Guide [12] provides navigaion service for reaching a visior-defined poin a exhibiion. Some recommender sysems focus on providing he personalized exhibiion. Cornelis e al [] proposed a mehodology for recommending rade exhibiion. I adops fuzzy logic for capuring he relaionships beween users and iems. Guo and Lu [] developed Smar rade Exhibiion Finder for suggesing he suiable inernaional rade V1-280

2 exhibiion o paricular businesses by inegraing he echniques of semanic similariy and he radiional collaboraive filering. However, he exising recommender sysems in he exhibiion environmen are saic since hey uilize he preinpued exhibi booh. herefore hey need o reflec a visior s dynamic preference on he exhibi booh over ime. B. ecommender sysem using Bayesian nework A Bayesian nework is a probabilisic graph model for relaionships among a se of objecs [7]. ha is, a Bayesian nework provides causal relaionship by he Bayes rule. As an example, suppose here are five variables A, C, D and E. Le us assume ha he condiional disribuion is given as follows; P(C A)=2/3, P(C B)=1, P(D A)=1/2, P(E C)=1 he srucure of he Bayesian nework is shown in Fig 1. he circles correspond o he variables and he arrows indicae ransiion probabiliies from one variable o oher variable. For example, he ransiion probabiliy from variable A o variable C is 2/3 bu he ransiion probabiliy from variable A o variable D is 1/3 A Bayesian nework has used in he sudies of he recommender sysems. hese sudies show ha a Bayesian nework provides a flexible mehod for a visior s behavior predicion [3][4][9][1]. herefore, we adop a Bayesian nework o discover he exhibi booh ha visior is more likely o see a he ime based on his/her previous behavior in he exhibiion environmen. III. MEHODOLOGY A. Overview Our sudy assumes he ubiquious exhibiion environmen which can rack he locaions and roues of he visiors in real-ime. hus a visior is able o receive informaion of he suiable exhibi booh o his/her preference hrough he ubiquious device. he underlying key concep of our proposed mehodology is adoped from he work on analyzing a visior's behavior over ime. A Bayesian nework model is adoped o analyze a visior's behavior. In general, a Bayesian nework provides he casual influence among a se of objecs. herefore, we are able o predic he visior s behavior a he specific ime. In oher word, a finie number m of he preceding exhibi boohs is used o idenify he visior s behavior and hen recommendaion of he exhibi booh for a arge visior is made a he ime. Figure 2. Overall Procedure he proposed mehodology consiss of he following five seps shown in Fig 2. In he firs sep, we preprocess daa o be colleced from he ubiquious exhibiion environmen for exracing a visior s preference on he exhibi booh. We assume ha he ime spen in fron of he paricular exhibi booh is he crucial indicaor [11]. In he second sep, we idenify he visiors behavior sequences as ime passes. hese visiors behaviors are profiled as a rajecory, which makes i possible o rack he dynamics of visiors behaviors. In he hird sepayesian probabiliies are calculaed based on he rajecory of visiors behaviors. And hen we consruc Bayesian neworks using he rajecory. In he fourh sep, we compue similariy beween rajecory of he arge visior and ha of he ohers during he previous m exhibi boohs before. And hen, we generae he op-n exhibi boohs ha he arge visior is mos likely o see in he Bayesian nework. B. Preprocessing We idenify a visior s preference on he exhibi booh in he preprocessing sep. In general, he ime spen in fron of he paricular exhibi booh is imporan o idenify a visior s preference [11]. If he ime spen in fron of he paricular exhibi booh is shor, i may be assumed ha he visior doesn prefer he exhibi booh. herefore we assume ha he visior is ineresed in he booh if a visior spen much ime in fron of he paricular exhibi booh. As an example, suppose ha visior i spen ime in fron of he exhibi boohs as able I. Le us assume ha he visior i is ineresed in he exhibi booh if he spen ime is over 00 seconds. hen, he visior i prefers he exhibi boohs B 2 3 and B because he spen imes in fron of he exhibi booh B and B are 119 seconds, seconds, 839 seconds, 299 seconds, 9 seconds and 1721 seconds, respecively. ABLE I. AN EXAMPLE OF HE IME WHICH VISIO I SPEN IN FON OF HE EXHIBI BOOH Exhi booh_id Enrance Deparure B 1 10:08:10 10:10:09 B 3 10:21:41 10:3:40 B 10:42:01 10:3:00 B 2 10:10:41 10:21:3 B 4 10:3:41 10:41:40 B 10:4:30 11:23:11 Figure 1. An Example of a Simple Bayesian Nework Srucure. V1-281

3 C. Idenificaion of visiors behavior rajecory All visiors have a behavior rajecory based on he previous exhibi booh. ha is, exhibi booh which a visior will see a he ime, depends on conex consising of a finie number m of he preceding exhibi boohs. We assume ha an exhibiion consiss of q boohs as follows; B { B1 2 q } (1) Le i be he behavior rajecory of he visior i. Behavior rajecory of he visior i is define as follow; i i, m i, 1 i, (2) i, k, k 0,1, 2,..., m,, m 0 For example, he behavior rajecory of he visior i, i, is . If m=2 hen he previous wo exhibi boohs is used o calculae a Bayesian probabiliy. ha is, i means ha if he visior i firs saw B 2 and second saw B 3, probabiliy ha he/she will see B nex is high or if he visior i firs saw B 3 and second saw B, probabiliy ha he/she will see B nex is high. D. Compuaion of exhibi booh sequence probabiliy We use a Bayesian probabiliy o calculae probabiliy ha a visior will see he specific exhibi booh a he ime because probabiliy o see he specific exhibi booh, depends upon he previous m exhibi boohs, Le BP be Probabiliy o see he exhibi booh a he ime, given he previous m exhibi boohs. hen BP is defined as follow; BP B m 1 ) (3) k, k 0,1,2,..., m, m 0 However, here is a scalabiliy problem if all he Bayesian probabiliies are compued. Objec of his sudy is o recommend exhibi booh o a arge visior a he ime when he arge visior saw an exhibi booh a he ime -1. Accordingly, we modify BP as follow; BP B m 1 ) (4) k,, k 0, 1, 2,..., m, m 0, bu B 1, 1 E. Similariy Compuaion o predic a arge visior s behavior rajecory, i is necessary o know he degree o which he behavior rajecory of he arge visior during is similar o he condiional par of he BP. Le be he behavior rajecory of he arge visior and BC be a condiional par of he BP. and BC for he previous m exhibi boohs is define as follows;, m, 1 () BC m 1 () We define he similariy beween and BC as follows; Le S(BC, ) be similariy beween BC and. S k 1 / 2 0 k m S ( BC, ) S (7) k 1 if k oherwise BC k B, k he above definiion indicaes ha, if he previous k h exhibi booh in he behavior rajecory of a arge visior is equal o he previous k h exhibi booh of he condiional par of BP, hen S -k is (1/2) k bu is oherwise equal o zero. F. Generaion of op-n exhibi booh recommendaion lis For each arge visior, his sep involves a op-n recommendaion lis of exhibi boohs ha he arge visior is more likely o see in he exhibiion environmen. We generae he op-n recommendaion lis based on BVLS(,B q ), which denoes booh visi likelihood score of he arge visior for he exhibi booh q. We compue he BVLS as follows; BVLS (, Bq ) { BP S ( BC, )} / S ( BC, ) (8) P is a Bayesian probabiliy ha a arge visior will see he exhibi Booh q a he ime. he higher he BVLS, he higher probabiliy ha a visior will see he exhibi boohs. herefore, we sor he exhibi boohs according o heir BVLS and reurn N boohs wih he high BVLS as he recommendaion se IV. ILLSAIVE EXAMPLE o help undersanding of he proposed mehodology, we presen a simple example of exhibi booh recommendaion in ubiquious exhibiion environmen. We suppose ha here are six visiors and en exhibi boohs. And we assume he each visior saw exhibi boohs as shown in able II. ABLE II. LOG DAA OF EACH VISIO Visior ID Exhi booh ID Enrance Deparure B 3 10:08:10 10:19:00 B 4 10:19:10 10:28: B 10:29:10 10:47:0 B 10:48:00 11:18:00 B 7 11:20:00 11:29:00 B 1 9:10:2 9:19:30 B 2 9:21:30 9:37:10 B 3 9:41:10 9:49:20 B 9 9:0:10 10:08:13 B 4 10:08:23 10:17:0 B 10:17:17 10:27:43 B 10:28:1 10:38:30 B 10 10:39:4 10:0:13 B 8 10:0:9 10:1:04 B 1 9:1:23 9:2:33 B 9:28:1 9:37: B 9:38:47 9:48:00 B 4 9:48:11 9:1:8 B 9 9::41 10:04:02 B 1 10:03:1 10:08:23 B 2 10:08:7 10:17:2 B 3 10:18:27 10:24: B 4 10:2:19 10:3:28 B 10:3:33 10:47:12 B 10:47:11 10:9:12 B 7 11:01:11 11:18:12 B 2 11:02:03 11:07:38 B 1 11:07:47 11:23:21 00 B 11:23:27 11:34:0 B 11:3:19 11:47:04 B 8 11:48:49 11:8:19 B 1 10:20:27 10:33:10 B 2 10:34:10 10:39:20 B 3 10:40:20 10:48:3 00 B 4 10:49:03 10:8:01 B 10:8:17 11:07:43 B 11:13:01 11:33:31 B3 11:3:4 11:4:13 V1-282

4 ABLE III. EACH VISIO S PEFEENCE ON HE EXHIBI BOOH Visior ID Exhi booh ID Enrance Deparure B 3 10:08:10 10:19:00 B 4 10:19:10 10:28: B 10:29:10 10:47:0 B 10:48:00 11:18:00 B 7 11:20:00 11:29:00 B 1 9:10:2 9:19:30 B 2 9:21:30 9:37:10 B 9 9:0:10 10:08:13 B 4 10:08:23 10:17:0 B 10:17:17 10:27:43 B 10:28:1 10:38:30 B 10 10:39:4 10:0:13 B 1 9:1:23 9:2:33 B 9:28:1 9:37: B 9:38:47 9:48:00 B 9 9::41 10:04:02 B 2 10:08:7 10:17:2 B 4 10:2:19 10:3: B 10:3:33 10:47:12 B 10:47:11 10:9:12 B 7 11:01:11 11:18:12 B 1 11:07:47 11:23:21 B 11:23:27 11:34:0 00 B 11:3:19 11:47:04 B 8 11:48:49 11:8:19 B 1 10:20:27 10:33:10 B 3 10:40:20 10:48:3 B 4 10:49:03 10:8:01 00 B 10:8:17 11:07:43 B 11:13:01 11:33:31 B 3 11:3:4 11:4:13 ABLE IV. BEHAVIO AJECOY OF EACH VISIO WHEN M=3 Visior_ ID B 3 B 4 B B 001 B 4 B B B 7 B 1 B 2 B 9 B 4 B 2 B 9 B 4 B B 9 B 4 B B B 4 B B B B 1 B B B B 2 B 4 B B B 4 B B B 7 00 B 1 B B B 8 B 1 B 3 B 4 B 00 B 3 B 4 B B B 4 B B B 3 ABLE V. BEHAVIO AJECOY OF EACH VISIO WHEN B I, -1=B Visior_ID B 4 B B B 7 B 4 B B B B 1 B B B B 4 B B B 7 00 B 1 B B B 8 00 B 4 B B B 3 A. Preprocessing o infer visiors preference on he exhibi booh, we assume ha he each visior is ineresed in he exhibi booh if he spen ime is over 00 seconds. hen, each visior s preference on he exhibi booh is as shown in able III. B. Idenificaion of visiors behavior rajecory Afer discovering he each visior s preference on he exhibi booh, we search all visiors behavior rajecories based on he previous exhibi boohs. Le i be he behavior rajecory of a visior i. 001 =, 002 = 003 =, 004 = 00 =, 00 = Exhibi booh, which a visior will see a he ime, depends on conex consising of a finie number m of he preceding exhibi boohs. If we assume ha a finie number m of he preceding exhibi boohs is 3, behavior rajecory of each visior is as shown in able IV. C. Compuaion of exhibi booh sequence probabiliy Objec of his sudy is o recommend exhibi booh o a arge visior a he ime when he arge visior saw an exhibi booh a he ime -1. Le he behavior rajecory of a arge visior be =. Because of m=3, we only consider =. ha is, is <B, -3 =B 4, -2=B, -1 =B >. o calculae exhibi boo sequence probabiliy, we use a Bayesian probabiliy. However, we only compue a Bayesian probabiliy when B, -1 =B, o solve he scalabiliy problem. In oher words, we calculae a Bayesian probabiliy using able V, which is behavior rajecory of each visior when B i, -1 =B hus BP is as follows; B B B B B ) 1 / 4 ) 2 / 4 ) 1 / 4 ) 1 / 2 ) 1 / 2 he srucure of a Bayesian nework is shown in Fig 3. D. Similariy Compuaion When =<B,-3 =B 4,-2 =B, -1 =B >, he similariy beween and BC as follows; S ( , ) 7 / 8 S (, ) 3 / Accordingly, we find ha S(, ) is higher han S(, ). Figure 3. A Bayesian Nework when B i, -1 =B V1-283

5 ABLE VI. BVLS B 3 B 7 B 8 B 9 B E. Generaion of op-n exhibi booh recommendaion lis Afer compuing he similariy beween and BC, we calculae BVLS o generae a op-n recommendaion lis of exhibi boohs ha he arge visior is more likely o see. he BVLS is represened in able VI. Suppose ha he size of he recommendaion is 3. As he resul, exhibi boohs B 7, B 8 and B 9 are seleced as he recommendaion se because he exhibi boohs have high BVLSs. V. EXPEIMENAL EVALAION We colleced oal 10 cusomers real visi daa a he Kid & Edu Expo 2010 from 4h November 2010 o h November 2010 in Korea. We employed 10-fold cross validaion approach and F1 measure for our es. In 10-fold cross validaion, he iniial daa are randomly pariioned ino 10 muually exclusive subses which have approximaely equal size. raining and esing is performed 10 imes. Our experimenal resuls are shown in Figure 4. An ineresing observaion from Figure 4 is ha performance of recommendaion improves as we increase he number of m size, and is good regardless of he number of m size when op-n size is 2. We can see ha recommendaion based a Bayesian nework reflecs he visior s dynamic preference and is applied o ubiquious device wih small screen size. VI. CONCLSION AND FE WOK he exising recommender sysems in he exhibiion environmen are saic since hey uilize he pre-inpued exhibiion or exhibi booh. herefore hey need o reflec he visiors dynamic preference on he exhibi booh over ime. o address his problem, we sugges a recommendaion mehodology based on a Bayesian nework because a Bayesian nework provides causal relaionship. he mehodology is composed of five seps; preprocessing of booh visi log, idenificaion of booh sequence, compuaion of booh sequence probabiliy, Similariy compuaion and generaion of a op-n booh recommendaion lis. Figure 4. Impac of m Size a Each op-n he proposed mehodology is expeced o offer he adequae recommendaion booh o visior s preference in he ubiquious exhibiion environmen. As fuure work, we plan o compare our suggesed mehodology wih one of ousanding approaches. ACKNOWLEDGMEN his work was suppored by he Knowledge service biquious Sensing Nework(SN) Indusrial Sraegic echnology developmen program, , Personalizaion markeer for an inelligen exhibi markeing funded by he Minisry of Knowledge Economy(MKE, Korea) EFEENCES [1] Abowd, G. D., Akeson, C. G., Hong, H., Long, S., Kooper,. and Pinkeron, M. (1997). Cyberguide: A mobile conex-aware our guide. Wireless Neworks, Volume 3, Issue, pp [2] Browning, J.M., Adams,.J. (1988). rade shows: an effecive promoional ool for he small indusrial business. Journal of Small Business Managemen, Vol. 2 No.4, pp [3] Chen, Y.H. and George, E.I. (1999). A Bayesian model for collaboraive filering. 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