Multi-Collaborative Filtering Algorithm for Accurate Push of Command Information System

Transcription

1 Mult-Collaboratve Flterng Algorthm for Aurate Push of Command Informaton System Cu Xao-long,, Du Bo, Su Guo-Png, Yu Yan Urumq Command College of CPAPF, Urumq 83000, Chna XnJang Tehnal Insttute of Physs and Chemstry Chnese Aademy of Senes,Urumq 8300, Chna Abstrat One of the most mportant ndators to measure the level of modern ommand nformaton system based on platform and data s that t has the ntatve and aurate push ablty of ommand nformaton. At present, the ommonly used ollaboratve flterng algorthm as a representatve of the personalzed reommendaton tehnology, has good applaton prospets. However, when ollaboratve flterng relatonshp establshed between ommanders and ommand elements, ollaboratve flterng algorthm faes the problems of data sparse and old start, whh wll ause not only the low effeny, but also the dffultes to guarantee auray. Ths paper proposes a msson-orented mult-ollaboratve flterng algorthm. Frstly, the algorthm performs tem-based ollaboratve flterng by operaton type on the ommand elements, and then ntegrate the oheson subset analyss nto user-based ollaboratve flterng to mne the smlartes between the ommanders and ommand elements under spef operaton types, thus to aheve aurate reommendatons. The expermental results show that the proposed algorthm an be appled to the ommand nformaton system of operatonal msson effetvely and mproves the reommendaton effeny and auray of the system. Keywords: Command System; Collaboratve Flterng; Coheson Subset; Soal Network Analyss. INTRODUCTION However, wth the explosve growth of data, CIS (Command Informaton System) exposes a seres of problems n nformaton sreenng, pushng and real-tme applatons, manly nludes: () Wth the growth of data amount and the development of related data projets, a large number of strutured and unstrutured data oexst n CIS and the apaty of data pool s huge, the orgnal query algorthms are dffult to hoose the real-tme ommand nformaton aurately requred by ommanders. () Varous types of operaton mssons hange rapdly. The tradtonal nput requrements --- system query---outome feedbak mehansm annot satsfy the requrements of mssons, so the ommand system should be qualfed wth the ablty to reommend aurate ommand nformaton ntatvely. (3) Due to the lmtaton of bandwdth of ommunaton network Broadband, the smooth ommunaton annot be guaranteed durng wartme, so the real needed data should be ensured to transmt to the ommanders. (4) the dfferent ommand nformaton are requred by dfferent ommanders of dfferent levels and dfferent haratersts, n dfferent regons, to arry out dfferent tasks n dfferent operaton perods, so t requres aurate push from ommand system n aordane wth the demand. How to solve the above problems? Currently, the reommendaton system beomng more mature, espeally the ollaboratve flterng algorthm, whh provdes us an dea of soluton. As the man algorthm of ommeral reommendaton system, the bas prnple of ollaboratve flterng s to predt the nterest of "target user" aordng to the nterest of "neghbor user", and then reommend the orrespondng "ommodty" to "target user". Aordng to ts lassfaton, the most ommonly used ollaboratve flterng reommendaton tehnologes manly nludes the ollaboratve flterng reommendaton based on users(luo, OUYANG and XIONG 00), the ollaboratve flterng reommendaton based on tems(deshpande and KARPIS 004), the ollaboratve flterng reommendaton based on models(zhang 007), ollaboratve flterng reommendaton based on the tme-weghtng(jin, QIANPING and KUN 007) and hybrd ollaboratve flterng reommendaton(mng and De-zh 0), et. Aordng to the reommendaton effet, ollaboratve flterng reommendaton has many problems n ommon suh as sparse, old start and real-tmng of data, whh dretly affet the effeny and auray of data reommendaton. Many sholars at home and abroad have made some researh and proposed some mprovement and optmzaton strateges (Jalre, Tosher and Legensten 00; Gong 00; L, Hao and Cheng 05; WANG 06; Goldberg and Roeder 00). These methods have made mprovements from the aspets of the smlarty judgment, the dmenson reduton of the ntal data, the mprovement of the sorng methods and so on, so as to mprove the reommendaton auray. As a speal "reommendaton system", ommand nformaton system s haraterzed by the varous types of data, the omplexty of nformaton group, the hgh real-tme requrements. Therefore, the researh on the ollaboratve flterng algorthm has ertan gudng sgnfane for the engneerng prate of ommand system development, on aount of the Command nformaton system spefed by the multple operaton tasks, multple nformaton elements and mult-level operatonal ommand

2 The nformaton system-based operaton desrbed n ths paper ontans three essental elements: ommander, ommand element nformaton, and operaton type. Prates of prevous operatons show that dfferent types of ommand element nformaton are orrespondng to dfferent types of the operaton, even for the same type of operaton, the requrements of ommander nformaton are dfferent from dfferent levels of ommanders. Wth the nreasng dversfaton of modern ombat operatons, a surge of element nformaton s needed. And the dret establshment of ollaboratve flterng relaton between ommander and ommand element nformaton has to fae the dlemma of data sparse and old start, whh wll ause not only the low effeny, but also the dffultes to guarantee auray. In order to solve these problems, ths paper proposes a Msson-orented Mult-Collaboratve Flterng Algorthm (MCFM). Frstly, to explore the ollaboratve relaton between operaton type and ommand element nformaton by Pre-Collaboratve Flterng Algorthm (P-CFA), and then to ntegrate the method of ohesve subgroup analyss of soal network analyss (SNA) nto the tradtonal ollaboratve flterng algorthm to mprove the smlarty alulaton method, whh wll solve the problem of data sparse and old start effetvely. Furthermore, t lays the theoretal foundaton for the realzaton of the aurate and real-tme push of the operatonal ommand essental nformaton, and the mprovement of the operatonal ommand abltes based on the nformaton system.. MISSION-ORIENTED MULTI-COLLABORATIVE FILTERING ALGORITHM. Fundamentals Mult-ollaboratve flterng algorthm framework for aurate nformaton push of ommand nformaton system s shown n Fgure.. Fgure. The framework of mut-ollaboratve flterng algorthm The framework onssts of two modules: pre-ooperatve flterng and seond-stage ollaboratve flterng. The bas prnple s, frstly, to extrat the operaton type and ommand element nformaton from the ommand nformaton system resoure pool and perform pre-ooperatve flterng, amng at the automat mnng of the top-n ommand element nformaton whh s most urgently needed by dfferent operaton types from the resoure pool under the preondton of ntal phase of msson and no preset of operaton type. Then, when the ommander (or superor ommand organ) makes the request, to onstrut the "Commander-Command Informaton Element" sorng matrx aordng to the bas haraterst nformaton of the ommander (Command - Level, loaton nformaton -Add, request tme -Tme, et.) and data request nformaton (operaton type -Task), ombnng wth the oheson subset analyss method to get a more aurate "neghbor set" from the omprehensve relatonshp matrx. Fnally, to make the predton sorng, whh s used to provde aurate element nformaton of operatonal ommand for the ommander. Seen from the prnple, n order to get a more aurate push results, the key ponts lays on the onstruton of sorng matrx onstruton and smlarty alulaton.. Smlarty alulaton based on ohesve subgroup analyss Soal Network Analyss (SNA) s a strutured analyss method that mnes the nternal struture of soal

3 networks by extratng relatonshp patterns among members of soety(wasserman and Fanst 994; Wellman 988). Cohesve subgroup analyss s an mportant researh dreton of soal network analyss. The ommander plays the major role n the operatonal ommand. The ommandng relatonshp between the dfferent ommanders s dstngushed and the ommand proess s lear. They all present a lear herarhal struture, but wth the nreasng dversfed types of operaton, the tendenes of flat, networked and ombned modularzed struture of ommand relatonshp beome more and more evdent. The applaton of ohesve subgroups whh have both hgh theoretal value and pratal value to lassfy the ommanders n ths paper serves a purpose to onstrut ohesve subgroup based on user smlarty relaton. A. Determnaton of smlarty In ths paper, the smlarty alulaton, usng the ohesve subset analyss method, s based on that the ommanders at the same or near ommand level have hgh smlarty to the ommand element nformaton for the same type of operatons. Therefore, the smlarty ( Csm )between the ommanders (Commander) s defned as follows: Csm He( u, v) Area( u, v) Tme( u, v) () Whh u and v express Commander u and Commander v respetvely, varables, and beng the weght oeffent, satsfyng that 0, + + =, and, respetvely, affetng the proporton of ommand He( u, v ),operaton Area( u, v) and operaton Tme( u, v) n the smlarty alulaton. Ths paper fethes = = = 3. The related onstrants are defned as follows: (a) The ommand herarhy n operaton, s set nto 7 levels, orrespondng to the headquarters, orps (army), detahment (brgade and regment), battalon, squadron, platoon, squad respetvely. The ommand relatonshps of dfferent ommand levels were haraterzed by formula (), the membershp degree of the 7 levels haraterzed by (0., 0., 0.3, 0.4, 0.6, 0.8, )..0 Frst level membershp 0.8 Seond level membershp 0.6 Thrd level membershp He( u, v)= 0.4 Fourth level membershp 0.3 Ffth level membershp 0. Sxth level membershp 0. Seventh level membershp () (b) Command area Area( u, v) expresses the operaton area relatonshp of ommander u and ommander v, expressng n the same operaton area and 0 expressng n dfferent operaton areas. Area u expresses the operaton area of ommander u and Area, the operaton area ommander v. v 0,( Areau Areav) Same area Area( u, v)=,( Areau Areav) Same area (3) () operaton tme Tme( u, v) expresses the operaton tme relatonshp that ommander u and ommander v make requests, expressng that the requests are n the same operaton perod, and 0 expressng that the requests are n dfferent operaton perods. Tme expresses the request tme of the ommander u and u Tme, the request tme of the ommander v. v 0,( Tmeu Tmev) Sametme Tme( u, v)=,( Tmeu Tmev) Sametme (4) B. Smlarty matrx The ommander's smlarty matrx an be onstruted by (). In graph theory, the reahablty between nodes s usually expressed by matrx multplaton. The smlarty between ommanders shows that the

4 smlarty between target ommander and other ommanders has multple reahable paths, that s, there exst smlarty transfer relatons, nludng the dret smlarty and ndret smlarty, namely there exst relatonshps of level ommand and leapfrog ommand n operaton, as shown n Fgure. U AC U CB Fgure. The transferene of smlarty relatonshp In the smlarty alulaton based on ohesve subgroup analyss, smlarty relaton transferene between ommanders an be measured by smlarty matrx multplaton, that s, UU A UU expresses seond step A reahablty ndret smlarty relaton. Based on SNA fatonal ndret relatonshp theory, only seond-step reahablty ndret smlarty relaton between ommanders s onsdered n ths paper, the seond-step reahablty ndret smlarty expressng leapfrog ommand relatonshp. UU s used to denote the seond-step reahablty ndret smlarty relatonshp after matrx omputaton, where eah element UU represents the seond-step reahablty smlarty between ommander and ommander j. Thus, the smlarty between ommanders should be the sum of dret smlarty and seond-step reahablty smlarty, denoted byuua. In order to faltate the alulaton, t s neessary to de-dmensonalze the smlarty matrx, namely where: UUA n UUA n UUA UUA (5) S j n UUA, S ( UUA UUA ) j j n UUA expresses the smlarty after the standard devaton transformaton, the mean value beng 0 and the standard devaton beng. In order to ensure UUA of flutuatng wthn the nterval [0,], the standard devaton transformaton s needed, the formula beng as follows: UR UUA mn( UUA) m max( UUA) mn( UUA) m m (6) Where UR expresses the fnal smlarty between ommander and ommander j,ur beng the fnal smlarty matrx..3 Smlarty alulaton Smlarty alulaton s very mportant n the ollaboratve flterng algorthm, whh determnes the auray of the reommended results. It s usually haraterzed by osne smlarty, Pearson orrelaton oeffent and modfed osne smlarty. Consderng that there exsts default for the osne smlarty to the onssteny of the sore measures and the Pearson orrelaton oeffent needs to onsder the orrelaton oeffent between the samples and other fators, n ths paper, the modfed osne smlarty s used n the pre-ooperatve flterng, the mproved modfed osne used n the seond stage of ooperatve flterng, as shown n equaton (7). Sm( u, u ) UR j U U ( R R )( R R ), j, j ( R R ) ( R R ), j, j U j (7)

5 R x y, k, C, x, (8) Whh, R, and R j,, respetvely, expresses the sores of ommand element nformaton C to ommander and ommander j, alulaton method as shown n formula (8), wth x, ndatng the hstor request tmes of the ommand elements, y, beng the expert sorng value, and beng the weght value. In order to faltate the alulaton, ths paper fethes = =. UR ndates the fnal smlarty between the ommander and the ommander j. R and nformaton to ommander and ommander j. Rj ndates the sore mean value of ommand element.4 Predtve sorng method There are two methods to determne the neghbor set of the ommander (operaton type). In the pre-ooperatve flterng stage, ths paper adopts the method of sortng aordng to the smlarty magntude of the modfed osne, hoosng the former N msson types as the neghbor mssons of the target msson. In the seond stage of ollaboratve flterng, the threshold gven, when the smlarty between the ommanders s greater than, the ommander s regarded as the target ommander's neghbor, aordng to the smlarty alulaton formula (7), t shows (0,). Thus, we an get the neghbor sets of N and N n two stages. Its predtve sorng method, s as shown n equaton (9): P R u, u n n [ sm( u, )( R R )], sm( u, ) (9) (9) gves sores of the unused ommand element nformaton for the ommander, and then pushed the M pees of ommand element nformaton whh have hgher sores to the ommander..5 Error estmaton The qualty of nformaton push requres quanttatve evaluaton rtera. In ths paper, Mean Absolute Error (MAE) s used as the measure standard. Mean Absolute Devaton (MAE) measures the auray of the foreast by alulatng the devaton between the predtve sore and the atual sore. The smaller the MAE, the hgher the qualty reommendaton. Its formula s as shown n (0): MAE n p N q (0) Where the predted sore set s expressed by { p, p,, p n }, the atual sore set expressed by{ q, q,, q n }. 3. EMPIRICAL ANALYSIS 3. Bas ases The method desrbed n ths paper an solve problems of how to push ommand nformaton aurately when the troops ondutng mssons, takng an real operaton aganst terrorsm as an example: two armed terrorsts haked 3 hostages n the ty of W, drvng a van to rash temporary hek roadbloks on ZS road, ausng some setons of the urban roads paralyss, the publ extremely pan. The gang of terrorsts surrounded by pole, fled to the brdge near the textle mll, then abandoned the vehle nto nearby resdental buldngs, attemptng to onfront wth the pole by vrtue of the advantage of the terran. The stuaton was very rtal. Jont ommand headquarters ordered the armed pole detahment of W ty to move qukly nto the msson area to omplete the deployment of seal and ontrol, on-ste dsposal and hostage resue task. Aordng to the brefng of the stuaton, the detahment drafted that squadron and squadron3 sent 0 offers and solders respetvely to dspose ths task, the deputy ommander beng the ommander n hef. Arrvng at the msson area, the deputy ommander C, the leader E of Squadron and platoon leader F of Squadron 3 respetvely requested related data of the msson to the ommand enter through the ommand

6 termnal, n order to further develop a detaled operatonal plan. 3. Data Preparaton The data used n ths paper s derved from the resoure pool of ant-terrorsm ommand nformaton system establshed by some data engneerng. The resoure pool ontans 0 ommand elements suh as ant-hakng, ant-attak, ant-volene (Sao) haos and other dozens of ombat modes, the number of terrorsts, property, equpment, soal ondtons, weather, terran, 300 ommanders requests of the ommand elements for 9,000 tmes, and the sore reord 0,000 to the ommand elements gven by experts based on the number of requests and the orrelaton, the sore dstrbutng wthn the nterval of [0,5], the hgher the sore that ommander made n a operaton, the stronger the demand for spef ommand elements of under some operaton type. The above data s stored herarhally aordng to seven ommand levels, namely headquarters A, orps B, detahment C, brgade D, squadron E, platoon F, lass G, 3.3 Expermental Desgn and Result Analyss The smlarty relaton network of the ommand herarhy based on the ohesve subset analyss s shown n Fgure.3. Fgure 3. The seond step reahablty network of Target ommander Where Agent-Agent7 represents the 7 ommand levels, that s, the headquarters A, the orps B, the detahment C, the battalon D, the squadron E, the platoon F, the squad G, wth eah level onneted by the bdretonal arrow ndatng that ts nformaton flow s nteratve. Its smlarty ndex Csm s alulated by formula (), and the seond-step reahablty relaton of smlarty relaton s obtaned by matrx multplaton, and fnally the result s de-dmensonzed aordng to formula (5) and (6). A. Influene test of parameter In ths experment, the smlarty deson parameters n the seond-step ollaboratve flterng s hanged to observe the nfluene of ths parameter on the algorthm mentoned n ths paper, thus fnd the optmal value of the parameter. When N =3 s taken and N 0, 0,30 s taken respetvely, the smulaton results are shown n Fgure 4. Fgure 4. The mpat of parameter The followng onlusons an be drawn from the fgure: (a) Under dfferent neghbor numbers N, the reommendaton error MAE tends to be onsstent wth nreasng value ; (b) =0.3 s the optmal threshold

7 When 0.3, the error dereases wth nreasng value, When 0.3, there s a postve orrelaton between the error and ; () under the same value ondton, there s a negatve orrelaton between the error and N, when N s greater than 0, the nrease ampltude dereases. To ths end, we an feth 0.3, N =0 n the pratal applatons, so as to ensure the hghest auray and effeny of reommendaton n meetng the operaton requrements. B. The omparson test of MCFM and IBCF n reommendaton auray In order to further test the effetveness of the proposed algorthm, based on the test A (parameters nvolved are the optmum test parameters), the tradtonal tem-based ollaboratve flterng algorthm (Item Based-CF) s used as a ontrol group to make a omparson test, the results shown n Fgure.5. Fgure 5. Comparson of algorthms The fgure 5 shows that under dfferent numbers of ommand nformaton elements, the reommendaton errors(mae) of the msson-orented mult-ollaboratve flterng algorthm proposed n ths paper are all small, Compared wth those of the tradtonal tem-based ollaboratve flterng algorthm. It s lear that under the msson-orented ondtons, the proposed algorthm s effetve to mprove the auray of reommendaton. C. The omparson test of MCFM and IBCF n reommendaton effeny In order to verfy the atual operaton effeny of the method desrbed n ths paper, the dfferent numbers of ommand elements are seleted and the tradtonal tem-based ollaboratve flterng algorthm (Item Based-CF) was used as the ontrol group to make a omparson text, the results shown n Fgure6. Fgure 6. Comparson of algorthms effetveness The fgure shows that the average runnng tme of tradtonal tem-based ollaboratve flterng algorthm nreases sharply wth the nrease of the number of "neghbors tems", namely ommand nformaton, whle the runnng tme of msson-orented mult-ooperatve flterng algorthm s not only relatvely stable, but also far less than the tradtonal algorthms. It shows that the mult-ooperatve flterng algorthm proposed n ths paper s effent and stable, and an adapt to the retreval, flterng and reommendaton under dfferent sales of data, whh has a sgnfant effet on mprovng the effeny of ommand system reommendaton. 4. INCLUDE Experments show that the msson-orented mult-ollaboratve flterng algorthm an better adapt to the aurate push requrement of the ommand nformaton system, and mprove the effeny and auray of autonomous reommendaton of the system. In pratal operaton, the algorthm has also been tested and proved that the effet s good. In vew of multple operaton fores and the partularty of operaton mssons, - 7 -

8 the next step s to fous on the smlarty between ommanders n dfferent operaton fores, wth a vew to be effetvely appled to the ommand nformaton system of the jont operatons. ACKNOWLEDGEMENTS Ths paper s supported by the Natonal Natural Sene Foundaton of Chna (U6036) and the Natural Sene Foundaton of Xnjang (06D0A080). REFERENCE Luo Xn, Ouyang Yuan-Xn, Xong Zhang, et a. (00) The effet of smlarty support n K-Nearest-Neghborhood based ollaboratve flterng. Journal of Computer, 8(33): Deshpande M, Karps G (004) Item-based top-n reommendaton algorthms. ACM Transatons on Informaton Systems, (): Xuejun Zhang (007) Personalsed onlne sales usng web usage data mnng. Computers n Industry, 58: Jn Lu, Qanpng Wang, Kun Fang (007) An optmzed ollaboratve flterng approah ombnng wth tem based predton. Proeedng of the 007, th Internatonal Conferene on Computer Supported Cooperatve Work n Desgn, pp L Mng, Xu De-zh (0) A ombnaton of personal reommendaton algorthm based on projet and user. Mnature mroomputer system, 3(4): 6-63 Jalre M, Tosher A, Legensten R. (00) Combng predtons for aurate reommende systems. Proeedngs of the 6th ACMSIGKDD nternatonal onferene on Knowledge dsovery and data mnng. ACM, pp Gong S (00) A ollaboratve flterng reommendaton algorthm based on user lusterng and tem lusterng. Journal of Software, 5(7): L Hong-me, Hao Wen-nng, Chen Gang (05) Collaboratve Flterng reommendaton algorthm based on Improved Loalty-senstve LSH, Computer Sene, 05 (0): Wang Fuqang; Peng Furong; Dng Xaohuan; Lu Janfeng (05) Loaton-vased asymmetr smlarty for ollaboratve flterng reommendaton algorthm, Computer Applatons, 06 (): Goldberg K,Roeder T,Gupta D, et al. (00) Egentaste: a onstanttme ollaboratve flterng algorthm. Informaton Retreval, 4():33-5. S. Wasserman, K. Fanst (994) Soeal network analyss: Methods and applatons. ed. Seres. Cambrdge: Cambrdge Unversty Press. B. Wellman (998) Strutural analyss: From method and metaphor to theory and substane, n Soal strutures: A network approah. B. Wellman, S D. Berkowtz (eds.). Cambrdge Unversty press: Cambrdge