Research on Digital Library Personalized Information Service Model Based on Agent Model

Transcription

1 Reserch on Digitl Librry Personlized Informtion Service Model Bsed on Model Xu Yn Xi n Physicl Eduction University Xi n, Shnxi, Chin, guilinxuyn@yhoo.com.cn Journl of Digitl Informtion Mngement ABSTRACT: Tking the personlized informtion needs of users s the reserch object, this rticle introduced the individulized service process of digitl librry, estblished the model, built multivrite function by combining with the user preference model, nd expounded the process of personlized informtion cquisition, nlysis nd push. We could conclude tht digitl librry personlized informtion service system operted coordintely between ech module, nd hd complete chin of reltionships. It could discovery nd trck informtion resources independently, effectively solve the problem of informtion overlod, nd relize the optimiztion of user informtion needs. Ctegories nd Subject Descriptors: I.2.11 [Distributed Artificil Intelligence]: Multigent Systems; H.3.7 [Digitl Librries] Generl Terms: Informtion Service Keywords: Digitl Librry, Informtion Service Received: 21 July 2013, Revised 25 August 2013, Accepted 2 September Introduction Along with the development of the virtul informtion system, the user cn esily get informtion, but is difficult to obtin ccurte informtion, especilly for scientific reserch clients nd cdemic users. They urgently need the informtion service with personlity, high qulity which needs the librry to provide specilized nd personlized service ccording to the different requirements of the users. Personlized informtion service rises t the historic moment. It cn ctively provide users with the personlized informtion bsed on the user s interests nd hobbies, let the reder quickly nd ccurtely find needed informtion from the vst mounts of informtion, nd lern utonomously by using librry resources [1]. Under the conditions of personlized informtion service, the user cn mke the corresponding informtion ccording to their requirements, nd digitl librry cn lso ctively service the users nd improve the service qulity ccording to user s needs. However, personlized informtion service needs robust technicl support. is used to design personlized service model of the librry. Tking the user s the center, it reserches the user s behvior nd interest nd serches, orgnizes, selects nd estblishes trgeted, user-oriented personlized service mechnism. The librry ctively serches knowledge for the reder insted of providing informtion pssively, which hs improved the utiliztion of librry resources [2]. There re mny reserches on personlized informtion services of digitl librry in the world. On the personlized push service nd push system of the digitl librry, for exmple, My Librry system put forwrd by Cornell university includes three service contents: Linksmen, My updtes nd Contendents [3]. My librry system developed by zhejing university librry includes such functions s bookmrks, custom librry digitl resources, the ltest informtion, links, serch engine, nd custom WEB pge style. For the reserch of personlized recommendtion service, the typicl reserch projects t brod re: ihrrnin, Cite. The Seer, Fb [4]. Domestic reserch of this re is digitl librry personlized recommendtion system developed by School of informtion, renmin university of Chin nd librry. Adopting the, this rticle designs personlized service model, nd tkes the user s center to reserch the user s behvior, interests nd hbits, nd reserches the personlized informtion service model of digitl librry bsed on. 418 Journl of Digitl Informtion Mngement Volume 11 Number 6 December 2013

2 2. The Process of Personlized Service System in Digitl Librry The contents of personlized informtion service minly includes three modules: the customiztion nd retrievl of personlized informtion, nd personlized service interfce. Therefore, personlized informtion service system is structure which constntly circultes, nd minly hs three modules: the cquisition of user informtion, processing of personlized informtion nd personlized collection mngement nd scheduling which is shown in figure 1: DL collection nd is widely used in business, mnufcturing, finnce, e-commerce, etc. 3.2 The Function of Model technology provides the technicl support for the personlized informtion service of the librry. It hs such chrcteristics s utonomy, socility, ctivity, lerning, nd intelligent. It cn serch the network informtion resources ccording to user s request nd preferences, nd then filter, nlyze nd mine the informtion, finlly send to the user ccording to the priority of the correltion. becuse of its utonomy, it cn substitute the user to get ccess to the informtion ccording to user personlized informtion demnd without user intervention, nd then ctively push the informtion to the user which relize the fundmentl chnge of service mnner. user informtion collection informtion process informtion push 3.3 The Principle of Opertion Firstly, the user submits their informtion needs to intelligent retrievl. Figure 1. The process of personlized service system Digitl librry obtins the user s personlized informtion vi the custom, nd understnds the needs of users, nd then provides users with more ccurte informtion service. Tht is the process of informtion cquisition; nlyze the user s hbits, interests nd the informtion demnd, nd estblish ccurte profiles. At the sme time, keep trck of users condition, djust the content nd view of the digitl librry service in time, relize synchronous updting between user requirements nd informtion service. This is the process of personlized informtion; Personlized collection includes informtion push, mining nd prediction. Informtion push usully dpts the modeling method of user preference. In view of the new users who hve set the needed informtion, it sends the online informtion to the user regulrly or irregulrly by push technology, nd provides convenient conditions for the user. Informtion mining technology deeply nlyzes the informtion tht the user needs, nd then clssifies the informtion bsed on the chrcteristics of informtion content to improve the utomtion level of informtion retrievl. Informtion prediction nlyzes the previous informtion, then predicts the informtion point nd direction of future informtion. And then sends the predicted informtion to the user. 3. Estblishment of Model 3.1 The Conception of Technology is concept tht develops in the field of rtificil intelligence (AJ) [5]. It cn continue role of softwre entity which hs the cpbility of perception, problem solving nd communicting with the outside world. It cn utomticlly serch the informtion tht the user my be interest in ccording to the user-defined criteri, nd psses it to the specified plce ccording to the specified time. It becomes the medition of ccess to resources. technology is brnch of distributed computing, Secondly, strts the retrievl function, nlyzes nd understnds the user s informtion demnd, utomticlly goes into the digitl librry, tlks to librry server, nd retrieves, nlyzes nd processes the user interfce. Thirdly, optimize the results ccording to the needs nd the wy of thinking of users. Finlly, send the optimized results to the user. Intelligent is the ltest softwre design pttern which is beyond the technology of client/server oriented system. According to the design pttern of Agenÿintelligent retrievl designs the informtion ccess system which cn utomticlly retrieve the user interfce, dtbse of digitl librry nd the informtion resources ccording to the chrcteristics nd the wy of thinking of informtion users to dpt to the environment of the librry. 3.4 The Estblishment of Model The bstrct model of cn be expressed s: Ag: RE Ac, of which the set of the externl environment condition is: E = {e, e,... }, the ction set of is Ac = {,,... }, R is the performnce set of stte chnge of E nd Ac. According to the chnge of environmentl condition, the model mps stte to ction, nd mkes the motor output ct on the environment, to chieve closed loop. The specific process is shown in figure 2. see next stte ction Input perceptul informtion motor output Externl environment (E) Figure 2. The bstrct model of Strting from the initil internl stte i0, observes the externl environment stte e, genertes perception Journl of Digitl Informtion Mngement Volume 11 Number 6 December

3 user requirement feedbck to user return the results personlized service units provide informtion personlity nlysis find dtbse server devition mplifying djust informtion service units to obtin strtegy contrstive nlysis repository Figure 3. The cquisition process of gent personlized informtion function see (e), nd updtes the internl stte of through the function next, nd then chnges to next [i0, see (e) ], finlly compres with stte librry. selects n ction r through the ction { next [ i0, see (e) ] }, where r is lternting sequence in the environmentl condition e nd : r: e0 0 e1 1 e u 1 eu, where is the body of n ction. After the ction, continues to percept the outside world by see, updte the stte through next, nd select the ction performed through ction. 4. The Anlysis of Model Function 4.1 The Acquisition Process of Personlized Informtion The key of personlized service is the cquisition of the user s dynmic informtion. There re two wys to obtin the informtion for Intelligent : one is to regulrly poll Web log, nd regulrly collect user s informtion; the other is to trck the in rel time, finds the chnges nd cts timely to nlyze the user s personlized informtion. The specific process is shown in figure 3. According to user requirements, personlity nlysis connects with the dtbse tht cn store user informtion, entrusts them to find the informtion which mtches the model, judges through the knowledge bse relted rules to extrct the personlized informtion service, nd sends results to personlity nlysis, nd then djusts the weights of the lgorithm, finlly feedbck the results to the user[6]. When the user submits the ppliction of digitl librry, for exmple, retrieve clss in personlity nlysis will dd the digitl librry, nd gives certin weight, nd constructs structure of personlized informtion by resoning lgorithm. Then retrieve the informtion round the personlized informtion structure, extrcts the personlized informtion nd then feedbcks the nlysis to personlity nlysis. The result includes digitl librry, nd system forecst informtion, such s digitl librry, distributed librry, digitl librry construction, etc.. After the retrievl, djust the corresponding weights of knowledge resoning lgorithm. 4.2 The Anlysis Process of Personlized Informtion Personlized processing module is the core of the whole system, which is responsible for the norml opertion of the whole system. It minly processes user demnd, extrcts the content nd trnsltes into the informtion tht cn identify by the mchines. Its structure is s follows: User informtion collection minly collects dt from the subscriber interfce module, nd clssifies simply, nd then sends the collecting informtion to the personlity nlysis which is key prt of the personlized processing module. According to the corresponding rules of knowledge bse, it uses resoning lgorithms to process the collecting informtion, seprtes out the user interfce lyer Informtion collection Mngement Messge scheduling repository (rule bse) personlized nlysis respository user informtion bse Figure 4. Anlysis process of personlized informtion 420 Journl of Digitl Informtion Mngement Volume 11 Number 6 December 2013

4 personlized content, such s the user s personl informtion, journl website tht user often visits, the keyword used, the user s IP ddress nd frequency of ccess, etc. When the processing is completed, put the results into the informtion dtbse or pss it to the informtion presenttion module, nd chnge the knowledge bse. Knowledge bse is repository which minly sves the dtbse needed, processing results, nd the dt send bck to the user fter processing [7]. At the sme time, the knowledge bse is lso rule bse nd cn nlyzes, judges ccording to the rules. Informtion scheduling schedules the dtbse ccording to the chrcter prmeter through precess of personlity nlysis, nd selects the pproprite informtion for the user, menwhile, feedbck the user s ctivity informtion to the personlity nlysis, nd reprocess by the personlity nlysis. Mngement is responsible to coordinte nd mnge other s. 4.3 The Push of Personlized Informtion of The min function of push dule is to recommend informtion to users fter the process of recommending module ccording to the previous informtion. The user preference modeling in personlized recommendtion system of digitl librry describes the integrtion of informtion requirements from submission to cncelltion, including dynmic renewl to ccurtely reflect the user s preference. User preference model cn be formlized s triple: P : P = (M, F, ψ) Of which M = {M (0), M (1),... M (M),... M (n) }, N = {F (1), F (2), F (M),... F (n) }, ψ : M F M, {M (M + 1) = (ψ M (i), F (i + 1) ), i = 0, 1,..., n} Here, M is the stte set produced in the process of use of the user preference model; F is the feedbck informtion set collected for the user preference model, nd its role is to drive the updting process; ψ represents the renewl function of the user preference model. Where, M (0) is the initil stte of the user model; M (i) mens user preference mode updted i times; F (i) is the informtion of updting M (i + 1) fter feedbck the ith times. The formlized description reflects tht the user preference model is updting constntly, nd synchronously flls the user s preference [8]. In view of the chrcteristics of user in digitl librry recommender system, this pper rises the question tht combines the short-term nd long-term interests. Set the long-term preference got bsed on their ge, professionl bckground, eduction level, etc; set the nlyzed preferences s the short-term preferences, such s the clssified preference, the smple documents nd query, nd so on. In the finl considertion of user preferences, we determine the preference model α M L + (1 α) M S, where M L is the eigenvector of long-term preference, M S M S is the eigenvector for short-term preference, α is the prmeter between (0 ~ 1), nd djusts the influence of the short-term nd long-term interests. Describe clssifiction system T s tree structure: T = (C, R), where C ={c1, c2,..., ci,...cn}, nd it is the set tht belongs to ll ctegories in the clssifiction system, ci is the i ctegory; R = R = { ci, cj r 1 < i < j < n}, which mens hyponymy mong the clssifictions in C nd ci, cj r mens ci is the clssifiction of cj. Bsed on the rule-bsed, we conclude tht the user my be interest in some ctegory, such s ci, cj, ck, etc, nd {ct1, ct2,..., cti,... } cn be pplied to ci. Thus M L cn be shown s M L = ci cj,... ck, = {ct1, ct2,..., cti} {ct 1, ct 2,..., ct i}... = {ct1, ct2,..., cti,... ct 1, ct 2,..., ct i} As to the condition tht the user selects multiple ctegories, we cn consider ccording to whether the preference ctegory is child-prent or sibling when we estblish the user preference model in the user preference model. In the condition tht the user selects multiple preference clssifiction, the initil preference model M S is expressed s: M L = α ci β cj = α{ct1, ct2,..., cti} β {ct 1, ct 2,..., ct i,...} = {αct1, αct2,..., αcti,... βct 1, βct 2,..., βct i,...} Where α nd β re the weights of preference of sibling clssifiction. As to the subclssing, we cn give n ttenution fctorγ (0 < γ < 1) shown s follows: M S = αγ {cj ci, cj r} β ci = {αγct1, αγct2,..., αγcti,... βct 1, βct 2,..., βct i,...} Recommended results cn be recommended to the user by push system (push), or obtined through the pull (pull). In the trditionl Internet services, informtion trnsmission ws processed ccording to the Pull mode, nd the serve provided by servicer is pssive. The user himself finds informtion. The Push mode provided by technology is ctive informtion service network, is the informtion for the user, the server for the user s informtion, tht is informtion finds users. The server gets the user s demnds nd pushes the informtion retrieved. technology service not only conveys informtion to users, but lso cn push users the updting nd dynmic informtion ccording to user s preset informtion chnnels nd requests to relize the rel personlized informtion service[9]. The reliztion of the individulized informtion service is to use intelligent technology to comprehensively mine the user s interests, nd utomticlly recognize nd extrct ccording to the user specific informtion object templte, nd then pss Journl of Digitl Informtion Mngement Volume 11 Number 6 December

5 the filtered results to the user in ccordnce with user requirements. Finlly relize the circultion of the whole service system. 5. Results nd Discussion From the bove nlysis, the whole personlized informtion system of digitl librry minly composes of three modules, nd the module is close relted with ech other, which hs formed complete chin of reltions. The whole system model is bsed on the user, nd tkes the coordintion between every link s the proof, which fully relized the personlized service of digitl librry. At present, the digitl librry personlized service is still in infncy, but s the digitl librry develops to networking, shring, users doesn t just fce with librry, but huge net-librry bsed on number of librries. It is difficult for user to obtin the ccurte informtion. The librry personlized service nd resource discovery nd trcking processed by the personlized service system independently, cn effectively solve the problem of informtion overlod, nd relize the optimiztion of informtion needs. Design the librry personlized service model with, hs high guidnce in reserching nd relizing the librry personlized service system, nd is good try is to relize the wide-re digitl librry personlized service. However, in terms of theoreticl reserch nd technology, the Internet oriented digitl librry personlized ctive informtion service is system engineering with multiinterdisciplinry, nd needs the integrted ppliction of kinds of theories nd technologies, such s informtion retrievl, rtificil intelligence, dt mining nd so on. Therefore, librries of ll types nd t ll levels should deeply reserches the relted problems on the bsis of the existing service, especilly the pplied reserch of the technologies, such s semntic Web, ontology, net, mbient intelligence, etc [10]. It cn solve the problems, such s how to mke use of ontology technology to ccurtely obtin the user preference informtion, how to crry out precise retrievl of informtion resources, how to semnticlly mtch the user preferences nd informtion resources, how to use semntic grid to solve contrdictions of the scle nd efficiency of the personlized ctive informtion service nd how to effectively crry out individulized ctive informtion service in the intelligence environment, etc. Obviously, only the effective technology integrtion, nd good solution to these problems,cn mke the user s the orgnizer of informtion resource, menwhile, uses the informtion efficiently nd enjoys convenient service. References [1] Lin, Ning. (2012). Reserch on the individul service model of the digitl librry on the bsis of the intelligent. The Journl of the Librry Science in Jingxi. (3) [2] Zhenxing, Hou., Hongyn, Cui. (2013). Reserch on personlized ctive informtion service model of digitl librry. Informtion Science. (3) [3] Weixin, Xu. (2013). Construction of personlized informtion service system of digitl librry. Journl of Nnchng College of Eduction. (2) [4] Yufeng, Zhng.,Chungye, Yn. (2010). Reserch on the model of personlized informtion service bsed on gent. Informtion Science. (5) [5] Yongxin, Li., Pengwei, Zhu. (2011). Reserch on personlized informtion push service model bsed on. Informtion Technology. (7) [6] Xiqun, Hung. (2006). The Modeling of User Profile in the Digitl Librry Recommender System. Informtion Science. (1) [7] Xiulin, Chen. (2008). induvidution cpcity informtion serches system be pplied in figure librry science flt roof. Science nd Technology of West Chin. (1) [8] Xioyong, Lin. (2006). Reliztion of personlized informtion service bsed on gent technology. Journl of Librry Science. (4) [9] Jun, Zhng. (2013). The ppliction of ElGml encryption technology to the informtion security of digitl librry. IAES Journl of Electricl nd Electronics Engineering. 11 (12). [10] Genjin, Yu., kunpeng, Weng. (2013). Intrusion detection system nd technology of lyered wireless sensor network bsed on. IAES Journl of Electricl nd Electronics Engineering. 11 (8) Journl of Digitl Informtion Mngement Volume 11 Number 6 December 2013