TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.12, No.4, Aprl 2014, pp. 3158 ~ 3167 DOI: http://dx.do.org/10.11591/telkomnka.v124.4786 3158 Context-aware Moble Recommendaton System Based on Context Hstory Qhua Lu School of Informaton Technology, Jangx Unversty of Fnance and Economcs, Nanchang 330013, Chna, 86-079183983891 emal: qh_lu@163.com Abstract Recommendaton systems for the moble Web have focused on endorsng specfcr content based on user preferences. But, user preferences vary n dfferent contexts, such as at dfferent tmes of day and n dfferent locatons. Therefore, n a moble networkng settng, provdng proactve personalzed servce s more lkely to depend on actual user context. Ths paper proposed a context-aware moble recommendaton system framework based on user models utlzng the context hstory. The approach was valdated n the toursm doman. From our experment and evaluaton, the proposed framework s a promsng approach to provder proactve personalzed servces to moble users. Moreover, ths research offers the personalzed servces to new users analyzng between the new user s nformaton and the stored assocaton rules. Keywords: moble web, recommendaton system, context-aware, context hstory, moble toursm 1. Introducton Recommendaton systems for the moble Web have focused on endorsng partcular types of content based on user preferences. However, moble devces can be used anywhere at any tme and n any context to connect to the moble Web. User preferences vary n dfferent contexts, such as at dfferent tmes of day and n dfferent locatons [1]. So, user context data are potentally useful for dentfyng the user s current needs n a sense that the contextual stuatng can be a crucal factor affectng user models. In a moble networkng settng, provdng proactve personalzed servce s more lkely to depend on actual user context [2]. Moreover, advances n moble technologes have made the collecton of customers' context nformaton feasble. To manage these context-senstve stuatons, recently, context-aware computng has been consdered to automatcally acqure and utlze context data n order to run the servces that are approprate for the partcular people, place, tme and events [2-3]. Many researchers have been nterested n context-aware computng [1-15]. Context s any nformaton that can be used to characterze the stuaton of an entty where an entty s a person, place, or object that s consdered relevant to the nteracton between a user and an applcaton, ncludng locaton, tme, actvtes, and the preferences of each entty [13]. Context awareness s about capturng a broad range of contextual attrbutes to better understand what the user s tryng to accomplsh, and what servces the user mght be nterested [11]. So, usng the context-aware technology to estmate user models are crucal for moble networkng applcatons. There were some prevous researches for the personalzed servces usng the users preferences on context-aware computng. However, most research only consder the current user context whle gnorng the context hstory. As the collecton of the past context and users actons for the past context, context hstory has been used for the predcton of future context, selecton of devces and adaptaton [8, 13]. So, the user s context hstory should be taken nto account n order to model and nfer ther needs n the present stuaton. Byun and Cheverst (2004) used Decson tree based on context hstory to nfer the preferences of the user [8]. But, t s dffcult to predct new user s preferences n ths research because ndvdual preferences were extracted and saved. There s also lack of specfc method to nfer user s preferences. Hong et al (2009) have suggested the basc drecton for provson of the personalzed servces on context-aware computng and utlzaton of context hstory [13]. They assume that the hghlevel context s already nferred n ther research, and extract the preferences of users usng the Receved August 28, 2013; Revsed November 19, 2013; Accepted December 9, 2013
TELKOMNIKA ISSN: 2302-4046 3159 survey about the preferred servces when the hgh-level context s gven. However, there are some lmtatons lke these: (1) Frst, accurate hgh-level context nference s notorously dffcult as dmensons of context may be dynamcally updated, whch makes t more complcated to handle the change of the user s dynamc models than the change of statc user models. (2) Second, ths research dd not provde the common formal and reusable representaton format of user preference and preferred servces, whch tends to lead to some semantc problems such as synonymy and polysemy. Ths paper proposed a context-aware moble recommendaton system framework based on user models utlzng the context hstory. Based on the proposed framework, we presented a system called CAMTRS, whch was a context-aware moble toursm recommender system. From our experment and evaluaton, the proposed framework s a promsng approach to provder proactve personalzed servces to moble users. Moreover, ths research offers the personalzed servces to new users analyzng between the new user s nformaton and the stored assocaton rules. 2. System Framework Context hstory s the collecton of the past context and users actons for the past context. The hstory of contexts could be extremely valuable n enablng the current level of context nterpretaton to be sgnfcantly enhanced [8]. For example, by usng hstores of context (such as user locaton, calendar nformaton, etc.), t may be possble to determne that a certan user has a regular meetng schedule. A proactve system could then take the proactve step of remndng the user of the meetng at an approprate tme before each meetng [13]. Fgure 1 decrbes the context-aware moble recommendaton system framework based on user models utlzng the context hstory. It has four phases: context hstory acquston, context hstory nference, preference predcton and user modellng, and recommender. Fgure 1. Context-aware System Framework for Proactve Personalzed Moble Networkng Applcatons Moble agents are software agents that are capable of transmttng themselves (ncludng ther program and ther state) across a computer network and recommencng executon at a remote ste [11]. Moble agents have been proposed to solve problems of networked applcaton domans, for example, electronc commerce [16], network management [17], and nformaton retreval [18]. As the moble applcatons are generally operated n an open envronment n whch resources and repostores are dstrbuted n dfferent machnes and the network connecton s Context-aware Moble Recommendaton System Based on Context Hstory (Qhua Lu)
3160 ISSN: 2302-4046 unrelable, the mult-agent desgn methodology thus provdes an approprate soluton to the deployment of such type of applcaton servces [11]. So, ths framework conssts of seven moble agents: user profles agent, user context agent, user nterest agent, context hstory agent, assocaton rules agent, user modelng agent, and recommender agent. 2.1. Context Hstory Acquston The acquston of context hstory nformaton depends on three sources: user profles, user context and user nterests [19]. Users profles refer to the users personal nformaton such as gender, age, natonalty, educatonal level and avalable ncome. User profles agent uses the form-fllng approach to collect the nformaton about the users profles. In the form-fllng approach, nformaton s acqured drectly from users nputs. User context s the central dmenson n contextualzed moble applcatons and the most wdely one addressed n the research area. User contexts consdered n ths paper are: locaton, tme, weather and devce. Although, there are no lmts of number of contextual dmensons n the context-aware applcatons, only four dmensons were used for the smplcty. These four dmensons were selected snce locaton, tme, weather, devce and network have been the most wdely used context dmensons so far. User context agent uses varous advanced moble and ubqutous technologes such as Sensor, RFID, Bluetooth, and GPS to collect four user contexts: locaton, tme, weather and devce. User nterests may be descrbed by the user behavor for the past context. User behavor, seen as set of mplct feedback ndcators such as past clck hstory, clckthrough data, browsng features and eye-trackng [20]. HUNC (Herarchcal Unsupervsed Nche Clusterng) [21] s a herarchcal verson of the unsupervsed nche clusterng algorthm, whch has been effectvely appled n web usage mnng and personalzed servce applcatons. So, User nterest agent apples HUNC algorthm on web log data to analyze the prevalent onlne actvty patterns and buld user nterests ontology based on doman ontology as Fgure 2. Use nterests are represented usng doman ontology based on Semantc Web technology and ontology markup language. Fgure 2. Buldng user nterests ontology based on doman ontology (1) Apple HUNC algorthm on web log data to acqure relevant web pages, (2) Use the method whch, gven a web page to be classfed, automatcally generates an ordered set of approprate descrptors extracted from the doman ontology based on [22], TELKOMNIKA Vol. 12, No. 4, Aprl 2014: 3158 3167
TELKOMNIKA ISSN: 2302-4046 3161 (3) Compute the clarty of each concept Clarty C wth the followng property: C C NumAttrbute 1 Clarty C 1 (1) NumSubConcepts NumAttrbu tec s the number of attrbutes of the concept C, NumSubConc eptsc s the number of the each leaf class of the concept C, (4) Compute the access frequency of each concept RFrequency (C) wth the followng formula: RFrequency j j Q ( URL ) ( C ) RFrequency ( URL ) (2) Q 1 1 RFrequency URL ) s the access frequency of the web page I, Q URL ) ( ( access number of the web page I, Q s the access number of the all pages, (5) Compute the DOI of each concept Interest (C) wth the followng formula: Interest( C) RFrequency( C) Clarty( C) (3) s the (6) Buld user nterests ontology based on doman ontology. User nterest ontology stores the degrees of nterest for each nstance and for each class refereed to the user nterest preferences. The degrees of nterest (DOI) of each class belong to the range [0, 1] and can be computed by consderng the sum of DOI for each leaf class. 2.2. Context Hstory Inference Context hstory nference layer nclude two moble agents: context hstory agent and assocaton rules agent. Ontology s a formal explct specfcaton of a shared conceptualzaton. It s a wdely accepted approach for context hstory modelng wth the benefts of: (1) sharng and reusng context knowledge, (2) gvng formal semantcs to context element whch enable formal analyss and reasonng, and (3) ndependence of programmng language [12]. Context hstory agent uses ontology based on OWL to represent context hstory nformaton. Because to manage and process lots of context nformaton on context-aware computng s dffcult and the amount of context nformaton, we use the herarchcal approach to buld context hstory ontology. Context hstory ontology conssts of common ontology and doman ontology n ths research as Fgure 3. Mtchell et al. (1994) argued that learnng based on a decson tree s sutable because the rules generated may be ntellgble to humans, whereas, learnng based on the neural network approach s less sutable because the weghts produced by ths approach are dffcult to nterpret by human users [23]. However, Setono and Lu (1996) suggest a method of symbolc rule representaton from the weghts obtaned from a neural network and emprcally showed that the rules generated by the augmented neural network and the rules extracted by a decson tree are remarkably smlar [24]. However, he also recommended that when the tme for learnng must be as short as possble, the decson tree approach s most approprate because t takes consderably longer to learn a rule set by a neural network than by a decson tree. Leva (2002) developed a mult-relatonal decson tree learnng algorthm (MRDTL) [25]. Experments reported by Leva (2002) have shown that decson trees constructed usng MRDTL have accuraces that are comparable to that obtaned usng other algorthms on several mult-relatonal data sets. However, MRDTL has two sgnfcant lmtatons from the standpont of mult-relatonal data mnng from large, real-world data sets: slow runnng tme and nablty to handle mssng attrbute values [26]. Aganst ths background, Atramentov (2003) descrbes MRDTL-2 whch attempts to overcome these lmtatons and has acheved better experment results. So, assocaton rules agent uses mult-relatonal decson tree nducton MRDTL-2 to mn context hstory for buldng assocaton rules knowledge base. Context-aware Moble Recommendaton System Based on Context Hstory (Qhua Lu)
3162 ISSN: 2302-4046 Step 1: Storng context hstory ontology n relatonal databases; Relatonal database systems are very mature and scale very well, and they have the addtonal advantage that n a relatonal database, ontology data and the tradtonal structured data can co-exst makng t possble to buld applcatons that nvolve both knds of data. In ths paper, we apply a novel approach to transformaton of context hstory ontology to relatonal databases, whch s proposed by [27]. Step 2: Usng MRDTL-2 to buld the mult-relatonal decson tree; Step 3: Convertng the mult-relatonal decson tree nto IF-THEN rules, and buldng assocaton rules knowledge base. Accrodng the study of Mollestad and Skowron (1996) [28], we pre-set a threshold u (0<u<1), and delete some rules wth a smaller credblty. Fgure 3. Context Hstory Ontology 2.3. Preference Predcton and User Modelng The development of personal networked moble computng devces and envronmental sensors mean that personal and context nformaton s potentally avalable for the personalzed applcatons. Accordng to users current context and personal nformaton, user modelng agent nfers users nterest preferences n specfc stuaton as Fgure 4, and bulds the user model. Fgure 4. Preference predcton and user modelng TELKOMNIKA Vol. 12, No. 4, Aprl 2014: 3158 3167
TELKOMNIKA ISSN: 2302-4046 3163 Step 1: Usng user profles agent and user context agent to collect user s personal and contextual nformaton n the present stuaton; Step 2: Usng assocaton rules KB to nfer users nterest preferences; Step 3: Buldng the personalzed user model based on doman-specfc ontology. 2.4. Recommender We use the content-based flterng approach to proactve provde personalzed nformaton to moble users. In ths paper, the User model s descrbed by the Semantc Vector based on ontology: U u u,..., u k. Ck ndcates the specfc concept of User model, and 1, 2 uk ndcates the DOI of k based on ontology: R R R,... R k. Rk ndcates the mportance of C to user u. The web pages are descrbed by the Semantc Vector 1, 2 Ck to the page R, can be determned wth the TF-IDF (Term Frequency-Inverse Document Frequency) measure method. Then, the cosne smlarty measure s used to compute the smlarty between U and R. The formula could be seen n the followng equaton: Sm U, R U U R R k 1 u k k 2 u 1 1 R R 2 (4) 3. Applcaton to Moble Toursm The toursm ndustry has always been open to new technologes; even more so to the moble networkng, gvng rses to the feld of moble toursm (m-toursm). M-toursm represents a recent trend n the feld of toursm that nvolves the use of tourst applcatons offerng servces and tours wth multmeda content executed on electronc moble devces, e.g., moble phones, personal dgtal assstants (PDA), palmtops, -pods and psp consoles [29]. Because the number of possble travel optons and sources of avalable nformaton s growng at a staggerng rate, t has become more dffcult to provde support to travellers at any tme durng ther trp: when they buld ther pre-travel plan, durng ther move to, or ther stay n, the selected destnaton, and possbly when the trp fnshes. In a moble settng, the characterstcs of the tourst recommendaton servces are [30]: a) The relevant nformaton to support the decson s dstrbuted. b) The nformaton sources are specalzed. c) The nformaton s updated frequently and n an asynchronous way. d) The query s done n real tme and the result needs to be effcent. So, personalzaton has been recognzed by researchers as a crtcal factor of effectveness, added value and commercal success n toursm. In moble toursm, personalzaton has manly been addressed n the context of gudes, provdng content recommendatons that match user preferences, typcally consoldated n user models. Accordng to the proposed context-aware system framework, ths paper proposed the CAMTRS-Context-Aware Moble Toursm Recommender System. CAMTRS s an moble networkng applcaton that serves a tourst wth nformaton needed n hs specfc context that are nterestng to hm gven hs goal for that moment. CAMTRS s composed of three sub modules: Tourst Informaton Collecton Module, User Modellng Module and Personalzed Recommendaton Module as Fgure 5. The User Modellng Module conssts of Context Hstory Acqustng Wrapper, Context Hstory Inference Wrapper and Preference Predcton Wrapper. Tourst Informaton Collecton Module collects tourst nformaton based on some tourst web stes. The Weblech crawler (http://weblech.sourceforge.net) s employed to crawl web pages. The seed URL s the web ste www.51yala.com, and the predefne topc s Travel. In ths paper, we collected 28954 Chnese web pages as the data set. User Modellng Module use MRDTL-2 to mn context hstory for buldng user model based on e-toursm ontology (http://e-toursm.der.at/ont/docu2004). Ths e-toursm ontology descrbes the doman of toursm and t focuses on attractons, accommodaton and actvtes. It Context-aware Moble Recommendaton System Based on Context Hstory (Qhua Lu)
3164 ISSN: 2302-4046 s based on an nternatonal standard: the Thesaurus on Toursm and Lesure Actvtes of the World Toursm Organzaton. Personalzed Recommendaton Module uses the content-based flterng approach to proactve provde personalzed tourst nformaton to moble users. Fgure 5. Program Structure for CAMTRS 4. Implementaton and Evaluaton The CAMTRS was mplemented usng Java under JDK1.6 and expermented on a HP ProLant DL388 G7 Server platform wth the LINUX operatng system. Context hstory ontology was stored n Mcrosoft SQLServer 2005. To collect users context hstory, we studed 200 Chna moble web users. The statstcal nformaton of the users s presented n Table 1. Table 1. The Statstcal Informaton of the Users Demographc characterstc n Percentage of the total Age 18 and under 8 4% 19-30 31-45 46-60 Over 60 Gender Male Femal Gducatonal level Bachelor s degree Master s degree Doctorate Post-doctorate Others 114 50 24 4 116 84 122 48 16 4 10 57% 25% 12% 2% 58% 42% 61% 24% 8% 2% 5% Avalable ncome (CNY) 500 and under 5 2.5% 500-2000 2000-10000 10000-50000 Over 50000 109 46 28 12 54.5% 23% 14% 6% Frst, we obtaned the users personal and contextual nformaton usng the form-fllng approach n moble ste of CAMTRS; only the form-fllng approach was used for the smplcty. Second, we obtaned the user nterest from user logs between July and September 2012. At TELKOMNIKA Vol. 12, No. 4, Aprl 2014: 3158 3167
TELKOMNIKA ISSN: 2302-4046 3165 last, the data of context hstory more than 38,000 records of 200 users n ths experment. Durng the context hstory nference, 368 assocaton rules are generated. In ths paper, we use the predctve accuracy metrcs to evaluate the CAMTRS. Measurng predctve accuracy are necessarly lmted to a metrc that computes the dfference between the predcted ratng and true ratng such as MAE(Mean Absolute Error). But, MAE may be less approprate for moble networkng applcaton. An nconvenent user nterface (small devces wth small screens and slow onscreen keyboards) may consttute a barrer to browsng the moble Web; therefore a ranked result s returned to the user, who then only vews tems at the top of the rankng. In a moble settng, users may only care about errors n tems that are ranked hgh, or that should be ranked hgh. It may be unmportant how accurate predctons are for tems that the system correctly knows the user wll have no nterest n. Ths paper uses the MAE (N) to measure the predctve accuracy of recommender system n the moble networkng applcatons. The formula could be seen n the followng equaton: N P U 1 MAE N (5) N In ths equaton, MAE (N) represents the mean absolute error of N tems that are ranked hgh n the result set; P ndcates a predcted ratng; U ndcates the user s true ratng. The threshold u s ether known nformaton must be fxed to facltate the evaluaton of results. We randomly selected 10 users, and computed the MAE (10) for u values of 0.1, 0.2, 0.3, 0.2, 0.5, 0.6, 0.7, 0.8, and 0.9. The results are shown n Fgure 6. The MAE (10) ntally decreases as a functon of K, reaches a peak at u=0.6, and then ncreases thereafter, so data are best accounted at the pont of u=0.6. Therefore, we choose the threshold s u=0.6. MAE(10) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 u Fgure 6. The Value of MAE (10) for Dfferent u Table 2. Archtecture of the Measured Models Model User profle User context User nterests Context hstory Model 1 No No Yes No Model 2 No Yes Yes No Model 3 No Yes Yes Yes CAMTRS Yes Yes Yes Yes To evaluate the performance of CAMTRS, we have tested four dfferent methods (Table 2). The frst one uses only the user s nterests, whch apples HUNC algorthm on web log data to analyze the prevalent onlne actvty patterns. The second one bulds the user model based on the user s nterests and hs current context [7]. The thrd one collects and accumulates user contexts as a context hstory, and nfers the users perferences usng Decson tree algorthm Context-aware Moble Recommendaton System Based on Context Hstory (Qhua Lu)
3166 ISSN: 2302-4046 [8]. The fourth one s the CAMTRS, whch ntegrated three man elements nto context hstory: users profles, user context, and user nterests. It s dffcult for the prevous researches (Model 1, Model 2 and Model 3) to provde new user wth the personalzed servces due to the defcency of ther hstory or nformaton. So, we nvestgated the performance of dfferent methods by performng a survey amongst 50 regstered users (42% females and 58% males). In ths experment, we computed the MAE (N) of four methods for N values of 5, 10, 20 and 50. The obtaned results were depcted n Fgure 7. 1 0.8 MAE()N 0.6 0.4 0.2 0 5 10 20 50 N Model 1 Model 2 Model 3 CAMTRS Fgure 7. Results Obtaned wth the Dfferent Models as Shown n Table 2 As can be seen n Fgure 7, the CAMTRS has the lowest MAE. So, CAMTRS has a better predctve performance than the other methods. Moreover, CAMTRS can offer the personalzed servces to new users analyzng between the new user s nformaton (user s profles and contexts) and the stored assocaton rules. 5. Concluson Ths paper proposed a context-aware moble recommendaton system framework based on user models utlzng the context hstory. The approach was valdated n the toursm doman. From our experment and evaluaton, the proposed framework s a promsng approach to provder proactve personalzed servces to moble users. Some lmtatons of ths research should be mentoned. Frst, CAMTRS uses the formfllng approach to collects users contexts for the smplcty. Second, CAMTRS uses the contentbased flterng approach to recommend tourst nformaton to moble users. But, t s necessary to verfy whether the recommendaton technque s reasonable or not. Most recommendaton technques fall nto three categores, namely content-based flterng, collaboratve flterng, and hybrd flterng approach. Future research can try to use dfferent recommendaton algorthms to observe whether the predctve performance of CAMTRS can be mproved. Acknowledgements Ths paper was supported by Natonal Natural Scence Foundaton of Chna (No: 71363022), Natural Scence Foundaton of Jangx, Chna (No: 20122BAB211024) and Foundaton of Jangx Educatonal Commttee (No: GJJ13290). References [1] Cho JY, Song HS, Km SH. MCORE: A Context-senstve Recommendaton System for the Moble Web. Expert Systems. 2007; 24(1): 32-46. [2] Kwon O, Km, J. Concept Lattces for Vsualzng and Generatng User Models for Context-Aware Servce Recommendatons. Expert Systems wth Applcatons. 2009; 36(2): 1893-1902. TELKOMNIKA Vol. 12, No. 4, Aprl 2014: 3158 3167
TELKOMNIKA ISSN: 2302-4046 3167 [3] Lu Q. U-Commerce Research: A Lterature Revew and Classfcaton. Internatonal Journal of Ad Hoc and Ubqutous Computng. 2013; 12(3): 177-187. [4] Schlt B, Adams N, Want R. Context-Aware Computng Applcatons. Proceedngs of IEEE workshop on moble computng systems and applcatons. Santa Cruz. 1994: 85-90. [5] Brown PJ. The Stck-E Document: A Framework for Creatng Context-Aware Applcatons. Proceedngs of the sxth Internatonal Conference on Electronc Publshng, Document Manpulaton and Typography. Palo Alto. 1996: 259-272. [6] Dey AK. Understandng and Usng Context. Personal and Ubqutous Computng. 2001; 5(1): 4-7. [7] Setten VM, Pokraev S, Koolwaaj J. Context-Aware Recommendatons n the Moble Tourst Applcaton COMPASS. Proceedngs of the thrd Internatonal Conference on Adaptve Hypermeda and Adaptve Web-Based Systems. Endhoven. 2004: 235-244. [8] Byun HE, Cheverst K. Utlzng Context Hstory to Provde Dynamc Adaptatons. Appled Artfcal Intellgence. 2004; 18(6): 533-548. [9] Kwon O, Yoo K, Suh E. ubes: Applyng Ubqutous Computng Technologes to an Expert System for Context-Aware Proactve Servces. Electronc Commerce Research and Applcatons. 2006; 5(3): 209-219 [10] Kocaball AB, Kocygt AK. Granular Best Match Algorthm for Context-Aware Computng Systems. The Journal of Systems and Software. 2007; 80(12): 2015-2024. [11] Lee W. Deployng Personalzed Moble Servces n an Agent-Based Envronment. Expert Systems wth Applcatons. 2007; 32(4): 1194-1207. [12] Gong R, Nng K, L Q, Sullvan DO, Chen Y, Decker S. Context Modelng and Measurng for Proactve Resource Recommendaton n Busness Collaboraton. Computers & Industral Engneerng. 2009; 57(1): 27-36. [13] Hong J, Suh E H, Km T, Km SY. Context-Aware System for Proactve Personalzed Servce Based on Context Hstory. Expert Systems wth Applcatons. 2009; 36(4): 7448-7457. [14] Santos AC, Cardoso JMP, Ferrera DR, Dnz PC, Chanho P. Provdng User Context for Moble and Socal Networkng Applcatons. Pervasve and Moble Computng. 2010; 6(3): 324-341. [15] Pcon A, Rodrguez-vaamonde S, Jaen J, Mochol JA, Garca D, Cadenas A. A Statstcal Recommendaton Model of Moble Servces Based on Contextual Evdences. Expert Systems wth Applcatons. 2012; 39(1): 647-653. [16] Dasgupta P, Narasmhan N, Moser LE, Mellar-Smth PM. MAgNET: Moble Agents for Networked Electronc Tradng. IEEE Transactons on Knowledge and Data Engneerng. 1999; 11(4): 509-525. [17] Papavasslou S, Pulafto A, Tomarcho O, Ye J. Moble Agent-Based Approach for Effcent Network Management and Resource Allocaton: Framework And Applcatons. IEEE Journal of Selected Areas n Communcaton. 2006; 20(4): 858-872 [18] Glpho RH, Olougouna E, Preer S. Moble Agents and Ther Use for Informaton Retreval: A Bref Overvew and on Elaborate Case Study. IEEE Network. 2002; 16(1): 34-41 [19] An W, Lu Q. Mnng Context Hstory for Generatng User Models for Proactve Personalzed Moble Networkng Applcatons. Appled Mechancs and Materals. 2013; 411-414: 638-642 [20] Tamne-Lechan L, Boughanem M, Daoud M. Evaluaton of Contextual Informaton Retreval Effectveness: Overvew of Issues and Research. Knowledge and Informaton Systems. 2010; 24(1): 1-34. [21] Nasraou O, Krshnapuram R. A New Evolutonary Approach to Web Usage and Context Senstve Assocatons Mnng. Internatonal Journal of Computatonal Intellgence and Applcatons. 2002; 2(6): 339-348. [22] Lus MDC, Alfonso ER. Bayesan Network Models for Herarchcal Text Classfcaton from a Thesaurus. Internatonal Journal of Approxmate Reasonng. 2009; 50(6): 932-944. [23] Mtchell TM. Machne Learnng. Columbus: McGraw-Hll. 1997. [24] Setono R, Lu H. Symbolc Representaton of Neural Networks. IEEE Computer Magazne. 1996; 29(1): 71-77. [25] Leva H A. MRDTL: A Mult-relatonal Decson Tree Algorthm. Master dssertaton. Ames: Iowa State Unversty. 2002. [26] Atramentov A. Mult-relatonal Decson Tree Algorthm-Implementaton and Experments. Master dssertaton. Ames: Iowa State Unversty; 2003. [27] Astrova I, Korda N, Kalja A. Storng OWL Ontologes n SQL Relatonal Databases. World Academy of Scence, Engneerng and Technology. 2007; 29(2): 167-172. [28] Mollestad T, Skowron A. A Rough Set Framework for Data Mnng of Propostonal Default Rules. Proceedngs of the 9th Internatonal Symposum on Methodologes for Intellgent System. Zakopane. 1996: 448-457. [29] Gavalas D, Kenters M. A Web-Based Pervasve Recommendaton System for Moble Tourst Gudes. Personal and Ubqutous Computng. 2011; 15(7): 759-770. [30] Lorenz F, Bazzan AL, Abel M, Rcc F. Improvng Recommendatons through an Assumpton-Based Multagent Approach: An Applcaton n the Toursm Doman. Expert Systems wth Applcatons. 2011; 38(12): 14703-14714. Context-aware Moble Recommendaton System Based on Context Hstory (Qhua Lu)