Feature Extraction for Collaborative Filtering: A Genetic Programming Approach

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1 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe Featue xtacton fo Collaboatve Flteng: A Genetc Pogammng Appoach Deepa Anand Depatment of Compute Scence Chst Unvesty Bangaloe Kanataka Inda Abstact Collaboatve flteng systems offe customzed ecommendatons to uses by explotng the nteelatonshps between uses and tems. Uses ae assessed fo the smlaty n tastes and tems pefeed by smla uses ae offeed as ecommendatons. Howeve scalablty and scacty of data ae the two majo bottlenecks to effectve ecommendatons. Wth web based RS typcally havng uses n ode of mllons tmely ecommendatons pose a majo challenge. Spasty of atngs data also affects the qualty of suggestons. To allevate these poblems we popose a genetc pogammng appoach to featue extacton by employng GP to convet fom use-tem space to use-featue pefeence space whee the featue space s much smalle than the tem space. The advantage of ths appoach les n the educton of spase hgh dmensonal pefeence nfomaton nto a compact and dense low dmensonal pefeence data. The featues ae constucted usng GP and the ndvduals ae evolved to geneate the most dscmnatve set of featues. We compae ou appoach to content based featue extacton appoach and demonstate the effectveness of the GP appoach n geneatng the optmal featue set. Keywods: Recommende Systems Collaboatve Flteng Genetc Pogammng Featue xtacton. Intoducton Recommende systems (RS) [8][] tackle the poblem of dscoveng nteestng and novel tems fom a hgh dmensonal tem space taloed to a use s tastes and pefeences. They can be categozed accodng to the type of nfomaton employed to ave at ecommendatons. Fo example content based ecommendes match potental tems fo the content smlaty wth the tems pefeed by the use n the past. Collaboatve Flteng systems[] on the othe hand leveage use affnty based on hstocal atng data to pedct the use pefeence fo vaous tems. The desablty of each tem fo a use s detemned based on ts appeal n the use s neghbohood whch compses of the set of uses whose taste match closely wth the cuent use. Collaboatve flteng systems ae futhe categozed nto memoy based and model based algothms [7]. Model based algothms use machne leanng technques to buld use models fom the avalable atngs data offlne and use the bult model onlne to offe ecommendatons. Memoy based algothms on the othe hand follow the lazy evaluaton stategy and pefom all computatons at the tme that the tems need to be ecommended. The pepocessng step used by model based algothms make them moe scalable than the memoy based countepats but they suffe fom the nablty to ncopoate the up to date atngs nfomaton. Thee have been attempts to fuse both memoy based and model based methods. Fo example [] and [5] popose to buld a condensed use pofle by leveagng on content based nfomaton of tems whch s used fo assessng smlaty between uses. K-Neaest neghbo appoach s then used to fnd the pedcted scoe fo actve uses. A majo ssue wth CF algothms s the hgh dmensonalty of featue space. The smlaty between a pa of uses s gauged by compang the pofles. The use pofle n a CF system conssts of the atngs by the use fo the dffeent tems. Snce the numbe of tems may be n ode of mllons the use pofle s lage and consequently the compason of use pofles fo smlaty estmaton mght eque tme. The use pofles n addton to beng lage ae also vey spase. The numbe of tems ated by uses s a small facton of the numbe of tems avalable n the system. Ths mples that the ovelap n atngs whle compang use pofles s small and thus may hampe ecommendaton qualty. Seveal solutons to the spasty have been poposed n the past such as employng tanstvty of smlaty to ncease smla use base [5] o usng addtonal nfomaton n the fom of tust [4] tags etc to estmate smlaty between uses not havng a lage tem ovelap. A soluton to both the ssues of spasty and hgh dmensonalty s to condense the use pofle nto a smalle dmensonal dense featue space. Methods have been poposed to condense the atngs space nto featue space by utlzng the content nfomaton assocated wth tems [][5]. We popose a genetc pogammng appoach to constuctng a compact use pofle usng the avalable Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

2 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe atng data. ach of the constucted featues s a functon of atngs fo a subset of tems fom the avalable tems. The featues ae evolved tll an optmal subset of featues wth a good dscmnatve capacty s acheved. The GP method scoes ove the content based featue extacton technques snce they ae not hndeed by unavalablty of content nfomaton n cetan domans. Moeove they may be able to captue latent featues whch may not be descbed by the content based featues. Addtonally the evolutonay appoach evaluates the goodness of the new featues as a goup. Ths s mpotant snce t s possble that content-based o newly constucted featues ae effectve n matchng smla uses ndvdually but ae not effectve as a goup. Moeove thee may be nteactons/ntedependences between these featues whch may detemne the effcacy of the featue set. Fo e.g. n the move doman whee moves may be descbed by the gene to whch they belong thee may be stong coelaton between pefeence fo omance gene and comedy gene. A good featue extacton scheme would account fo such nteactons by fo nstance consdeng only one of the coelated featues whle dscadng the othe. The poposed appoach evaluates a set of constucted featues as a whole and thus s expected to take cae of these ntedependences. The est of the pape s oganzed as follows: Secton dscusses elated wok. The poposed GP appoach s outlned n Secton 3 whle Secton 4 detals the expemental evaluaton. Secton 5 pesents the conclusons and ponts some dectons fo futue wok.. Related Wok. Genetc Pogammng Genetc Pogammng s a elatvely ecent technology whch has been demonstated as a vesatle tool fo Automatc Pogam Geneaton n a vaety of applcatons[[6]. GP belongs to a set of atfcal ntellgence poblem-solvng technques based on the pncples of bologcal nhetance and evoluton. ach potental soluton s called an ndvdual (.e. a chomosome) n a populaton. GP woks teatvely applyng genetc tansfomatons such as cossove and mutaton to a populaton of ndvduals to ceate moe dvese and bette pefomng ndvduals n subsequent geneatons [7]. ach membe n a populaton s assessed fo ts qualty by usng a ftness functon. The pogams evolved by GP ae geneally epesented as a tee. Moeove the sze of the dffeent chomosomes n the populaton may vay. Because of ts ntnsc paallel seach mechansm and poweful global exploaton capablty n a hgh-dmensonal space GP has been used to solve a wde ange of had optmzaton poblems that oftentmes have no known optmum solutons [7]. An nteestng applcaton of GP s n the aea of weathe foecastng whee the evapoaton loss s pedcted as a functon of vaous clmatc condtons [9].In the ecent past GP based technques have been hanessed n the aea of nfomaton and mage eteval. Fo example [] popose methods to constuct optmal classfes fo nfomaton eteval puposes. A GP based method to evolve mage smlaty measues fom exstng featues has been poposed by Toes et al. [7]. In the aea of ecommende systems [3] poposes a method to fnd optmum smlaty measues among uses whee the optmalty of a smlaty functon s detemned by the pedcton eo that t poduces.. Collaboatve Flteng Collaboatve flteng (CF) systems ae nsped by eal lfe decson makng pocess wheen we gathe opnons about nexpeenced stuatons/tems fom ou acquantances and fends and make an nfomed decson. The pedcton usng CF s pefomed n vaous stages. The uses ae fst assessed fo the smlaty wth othe uses. The pedcted scoe fo an tem by a use(actve use) s then pefomed by aggegatng the atngs of othe smla uses fo the patcula tem. The tems may then be aanged accodng to the pedcted scoe to be pesented to the use. CF systems tadtonally use Peason Coelaton Coeffcent to assess use smlaty. The smlaty computaton s pefomed usng the fomula; sm( x y) S S ( ( x x )( ) x x y S ( ) y y ) whee S s the set of tems whch uses x and y have co-ated and x s the mean atng fo use x. Vecto smlaty (VS) [7] on the othe hand s defned as ; sm( x y) S S x y x S y ffectve smlaty measues play a key ole n the ecommendaton pocess. Howeve as dscussed n the pevous secton the scacty of atngs data mples that fo seveal use pas the smlaty cannot be estmated at all o even f they can be they ae based on a vey small subset of common atngs and thus the elablty s a y () () Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

3 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe suspect. A vaety of solutons have been poposed to the spasty poblem. Addtonal nfomaton n the fom content detals of tems [] tust nfomaton [4] and tags assgned to vaous tems by vaous uses have been employed n the past fo enhancng the qualty of ecommendatons. Tanstvty of smlaty [5] has also been exploed n the past to enhance the use to use lnks. Most of the poposed methods howeve exclusvely addess the spasty ssue. The hgh dmensonalty of tem space hndes the ablty to offe tmely ecommendatons. Dmensonalty educton technques such as SVD [6] has been employed n the past to educe the use-tem space and offe tmely suggestons. Othe methods employ content based nfomaton to educe the tem space.[7] poposes to use tem content nfomaton to estmate the use s lkng fo each tem featue nstead of hs pefeence fo vaous tems. Snce the numbe of featues s a much smalle quantty than the numbe of tems the use pefeences can be expessed n a much compact fom. Moeove the pefeence fo tem featues would be dense snce f the use has expessed hs pefeence fo even a sngle tem wth the featue we would be able to estmate hs degee of lkng fo the featue. Anothe such method[] poposes estmatng use pefeence fo vaous move genes by utlzng hs pefeence fo vaous moves. A use who has vewed seveal moves of the gene Comedy fo example and ated them hghly wll have a hgh degee of pefeence fo Comedy. Snce the numbe of genes s a small quantty the use-tem pefeence space s educed nto use-gene pefeence space whch geatly educes the dmenson n addton to makng the matx dense. Ths matx s then utlzed to assess the smlaty between uses. Fo the fnal pedcton howeve the ognal atng matx s used. Ths method hence s able to combne the advantages of model based method (fast smlaty computaton) wth the benefts of memoy based algothms (usng up to date atng nfomaton fo atng pedcton). The dsadvantage of the above mentoned methods s that they ely on the avalablty of tem content nfomaton. Hence they ae not applcable fo domans n whch content nfomaton s non-exstent. We popose a method employng Genetc Pogammng to constuct a compact set of dscmnatve dmensons whch captue the latent featues n the data. The poposed method n addton to not elyng on avalable content nfomaton s also able to stoe the leant featues usng a constant space. 3. Genetc Pogammng based Featue xtacton (GPF) We popose to buld featues as a functon of tem atngs. Consde a system wth n uses U={u u un} and a set of m tems I = { 3 m}. Let the numbe of featues that an tem can contan be k F= {f f fk}. Let R be the atng matx and hence has dmensons nxm and C be the mxk matx such that Cj = f th tem contans jth featue. Usng genetc pogammng we exploe means to convet the nxm use-tem atng matx nto a nxk use-featue pefeence matx. Ths can be done n geneal by expessng the degee of lkng of a featue f by any use as a functon of pefeence of the same use f f f fo a subset of tems... l.e. f f f C( u f ) ( R( u ) R( u )... R( u l )) Ths s llustated though an example below. xample: Fg.(a) shows a use-tem atngs matx. Assume that the system s awae of the content based featues of the vaous tems as n Fg. (b) whch contans an enty fo a tem featue pa f the tem contans the featue. Then the use pefeence( say fo use ) fo each featue (say Featue ) can be estmated by examnng the atng confeed by use upon tems 3 and 4 snce these tems contan the featue. In ths patcula example the Fg (c) contans the featue pefeence fo each use whee the pefeence fo a featue s computed by summng the atngs of the use fo all tems contanng that featue.e. C( u Featue) R( u ) R( u 3) R( u 4)) C( u Featue) R( u ) R( u 5) Domans havng content based descpton of tems can utlze ths nfomaton to constuct a compact use pofle but fo domans not contanng ths nfomaton such an appoach s not possble. ven n the absence of content nfomaton t s possble that thee s a stong coelaton n the patten of atngs fo a set of tems snce they may belong to a common categoy. Usng genetc pogammng we thus endeavo to lean a set of functons whch map atngs fo subsets of tems to ndvdual featues. The next few sectons detal the chomosome epesentaton and the genetc opeatos used. Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

4 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe I I I3 I4 I5 U U U3 4 3 U U5 3 (a) I I I3 I4 I5 F F (b) F F U 0 4 U 3 5 U3 5 4 U4 6 4 U5 5 (c) Fg. (a) The use-tem atng matx. (b) The tem-featue matx. An enty of means that the featue s pesent n the coespondng tem (c) The constucted use-featue pefeence matx. 3. Chomosome Repesentaton The functon mappng the atngs fo a subset of tems nto a featue pefeence degee can be epesented as an expesson tee. g. Featue n xample can be epesented as shown n Fg.. Usng a combnaton of Genetc Pogammng and volutonay algothms a set of such featues each epesented by an expesson tee s evolved. An ndvdual I n the populaton thus s a set of xpesson tees I={.. x} whee x s the numbe of featues. The ntal populaton s chosen by andomly choosng the numbe of featues and geneatng the expesson tees coespondng to each featue. The leaf node n each expesson tee s ethe the atng fo an tem o a constant wheeas the ntenal nodes contan opeatos( ).The leaf nodes contan an tem atng. The lmt on the numbe of featues s fxed n the nteval [3 50]. 3. Genetc Opeatos New chomosomes n the populaton ae ceated by employng the genetcs-nsped opeatos of cossove and mutaton. Whle cossove facltates exchange of meanngful nfomaton among two paent tees and helps convegence to an optmal soluton mutaton helps n mantanng genetc dvesty n the populaton and avods beng tapped n local maxma/mnma. The pocess teates fo seveal geneatons tll a stoppng ctea s met [3]. Cossove The cossove opeato ae appled at two levels: Featue level and tee level. Consde two chomosomes C { x } C { and x }. The cossove at the featue level nvolves ntechangng a set of featues between the chomosome. A one pont cossove s followed wheen cossove ponts a and a ae chosen fom C and C espectvely. The new chomosomes geneated though ths type of cossove wll be C' { a a a y } and C' { a a a x }. The othe type of cossove nvolves choosng a featue (epesented by the expesson tee) each fom the chomosomes and pefoms a cossove of the expesson tees. Fgue 3 shows one such cossove. the of the two types of cossoves s pefomed wth the pobablty of choosng the methods beng equal. Mutaton One featue fom the chomosome chosen fo mutaton s alteed n two possble ways. the the featue s completely eplaced by a new featue(new expesson tee). The altenatve s to alte the tee coespondng to the featue chosen by eplacng a subtee by anothe. the of the two foms of mutaton s pefomed wth equal pobablty. Ftness Functon ach ndvdual n the populaton s assessed fo ts qualty usng a scoe known as the ftness value. The qualty of a chomosome s assgned based on ts ablty to solve the poblem at hand adequately. In the case of GPF the ftness of a chomosome s dven by the qualty of pedctons obtaned though t. To estmate the ftness of ndvduals n the populaton the atng data s dvded nto thee pats tanng set T valdaton set V and the test set T. The atngs n T ae teated as tems aleady ated by the use and s utlzed fo neghbohood constucton and atngs pedcton. The valdaton set on the othe hand s utlzed fo the leanng the optmal set of featues. The test set s used to evaluate the qualty of the leant featues and fo compason wth othe methods. Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

5 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe Step 3: stmate the smlaty matx sm(nxn) between all pas of uses. We use vecto smlaty q. to compute the smlaty. Step 4: Compute pedctons by usng the tanng matx T and the smlaty sm computed n step 3 usng Resnck s pedcton fomula[3] Fg : The featue constucton tee fo F sm * ( ) j j k j j N( ) p k sm j j N( ) It s to be noted that the unnng the genetc pogammng algothm to detemne the optmal featue set takes up a lot of computatonal esouces and s tme consumng but t s to be noted the optmal featue constucton pocess shall be pefomed as an offlne pocess whle the constucted featue set wll be used onlne fo pedctons. 4. xpemental valuaton Fg 3: Tee level Cossove The ftness fo an ndvdual s estmated by applyng the featue tansfomatons on the tanng matx (T) to get use-featue pefeence matx. Ths matx n tun s used to compute use smlates. The computed smlates ae used to pedct the atngs usng q. fo the set of atngs n V. The pedcton eo s then obtaned as the mean absolute dffeence between the pedcted and the actual atngs and s gven by ftness ( I) V ' V whee s the pedcted atng usng ndvdual I and s the actual atng n the valdaton set. GPF based Recommendaton Famewok Below we outlne the poposed appoach to ecommendatons based on GPF Step : Apply GP based on T and V to deve an optmal featue set C. Step : Apply the leant functons n C to the atngs n T to deve a use-featue pefeence matx. I We demonstate the effectveness of the GPF appoach by contastng the ecommendaton accuacy wth content based featue constucton technque as outlned n [7] and tadtonal measues of smlaty specfcally PCC [3] and VS[7]. MoveLens s a move atng dataset whch contans atngs gven to 68 moves by 943 uses. MoveLens also contans content nfomaton tems n the fom of genes that a move belongs to. Thee ae 8 genes that a move can belong to such as Romance Comedy Mystey etc. Snce we ae poposng a system fo move pedcton we follow the appoach outlned n [7] fo devng the nteest of a use n vaous featues (genes). Howeve we do not apply SVD snce the method s used fo ankng tems athe than pedcton. We hencefoth efe to ths method by Content based Featue xtacton (CBF). The appoaches ae compaed va the Mean Absolute o on T whch s defned as; T MA pk k T k (3) whee T s the numbe of atngs n the test dataset. p k s the pedcted atng fo the kth atng n the test set and k s the actual atng The atngs dataset s pepocessed to flte out uses who have ated less than sx tems. Snce spasty s one of the majo challenges facng CF algothms we study the effect of vaous levels of spasty n the data on the pefomance of the vaous algothms. To do ths we Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

6 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe MA R0 R30 R50 R70 R90 Confguatons PCC VS GPF CBF Fg 5. Compason of PCC VS GPF and CBF wth espect to MA on vayng spasty levels dvde the dataset nto tanng set valdaton set and test set. Fo each use we set asde two andom atngs each nto the valdaton set and test set espectvely. The est of the atngs belong to the tanng set. To ntoduce spasty we andomly emove R % of atngs fom the tanng set. We vay the R among the values n the set { } to get fve confguatons R0 R30 R50 R70 R90 espectvely. The numbe of neaest neghbos K s set to 0. Fo the GP the populaton sze s set to 0. The numbe of teatons s set to 5. The accuacy of the vaous methods unde vayng spasty levels s shown n Fg. 4. As s clea fom the fgue the poposed appoach (GPF) outpefoms all the othe methods unde all spasty levels. Wth ncease n the spasty levels the MA of all methods ncease whch s as expected. Among the dffeent methods CBF method outpefoms the tadtonal methods of estmatng smlaty. VS based smlaty measue pefoms the wost among all the methods. Fg. 4 shows the evoluton of the ftness along the vaous teatons. As seen fom the fgue ntally the decease n the MA s apd along the fst few teatons (wth the excepton of teaton 0 when the eo nceases). Towads the last fve teatons the decease s vey mno. Such expements wee pefomed wth othe confguatons. Though n some confguatons the numbe of optmal numbe of teatons s moe than ffteen we fx the numbe to ffteen snce we wsh to balance the qualty of ecommendatons wth the amount of tme taken to detemne the optmal featue map. 5. Conclusons and Futue Wok The am of the poposed wok s to tackle the poblem of hgh dmensonalty and spasty typcal to RS data. To ths end we popose a genetc pogammng based featue extacton technque to tansfom the use-tem pefeence space nto a condensed and dense use-featue pefeence space. The tansfomaton functons so constucted can model any lnea o non-lnea functon. The poposed appoach s able to combne the advantages of both memoy-based and model-based technques snce the condensed use pofle s employed fo use smlaty computaton wheeas the ognal tanng matx s used fo the atng pedcton. In addton to not elyng on content nfomaton to gude the featue constucton pocess the poposed technque also bases the evoluton of the featues as a set by thus enablng measuement of ndvdual goodness of featue as well as accountng fo nteactons theen. xpemental compasons demonstate the enhanced ecommendatons poduced by the poposed GP based appoach as compaed to content based featue constucton technques as well as tadtonal CF methods. In the futue we plan to study the effect of tweakng the ftness functon by accountng fo the coveage obtaned as well as othe measues such as dvesty []. We also plan to evaluate the appoach on moe vaed domans wth lage datasets. Domans whch eque good suggestons fo vaous uses wthout the Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.

7 IJCSI Intenatonal Jounal of Compute Scence Issues Vol. 9 Issue 5 No Septembe actual pedcted atngs utlze measuement based on classfcaton accuacy. The GP based method could be evaluated on ts effectveness n achevng hgh classfcaton accuacy. We also plan to employ othe appoaches nsped by genetcs such swam optmzaton and ant colony optmzaton technques[0] and popose to compae the vaous leanng technques n tems of the featue extacton abltes. Refeences [] A. A. Name and B. B. Name Book Ttle Place: Pess Yea. [] A. Name and B. Name "Jounal Pape Ttle" Jounal Name Vol. X No. X Yea pp. xxx-xxx. [3] A. Name "Dssetaton Ttle" M.S.(o Ph.D.) thess Depatment Unvesty Cty County Yea. [4] A. A. Name "Confeence Pape Ttle" n Confeence Name Yea Vol. x pp. xxx-xxx. [] G. Adomavcus and A.Tuzhln Towad the Next Geneaton of Recommende Systems: A Suvey of the Stateof-the-At and Possble xtensons I Tans. on Knowl. and Data n. Vol. 7 No pp [] M.Y.H. Al-Sham and K.K. Bhaadwaj Fuzzy-Genetc Appoach to Recommende System Based on a Novel Hybd Use Model xpet Systems wth Applcatons lseve Vol. 35 No pp [3] D. Anand and K.K. Bhaadwaj Adaptve use smlaty measues fo ecommende systems: A genetc pogammng appoach In: Poceedngs 3d I ntenatonal confeence on Compute Scence and Infomaton Technology I Chna 00 pp.-5. [4] D. Anand and K.K. Bhaadwaj. Punng Tust-Dstust netwok va Relablty and Rsk stmates fo Qualty Recommendatons Socal Netwok Analyss and Mnng Spnge 0 [5] D. Anand and K.K. Bhaadwaj xplong Gaph Based Global Smlaty stmates fo Qualty Recommendatons Intenatonal Jounal of Computatonal Scence and ngneeng Indescence(Accepted fo publcaton) [6] D. Bllsus and M. Pazzan Leanng collaboatve nfomaton fltes In Intenatonal Confeence on Machne Leanng Mogan Kaufmann Publshes 998. [7] J.S. Beese D. Heckeman aned C. Kade mpcal Analyss of Pedctve Algothms fo Collaboatve Flteng. In Poceedngs of the fouteenth annual confeence on uncetanty n atfcal ntellgence. Mogan Kaufmann 998 pp [8] R. Buke Hybd Recommende Systems: Suvey and xpements Use Modelng and Use-Adapted Inteacton Vol. 00 pp [9] K.S. KasvswanathanR. Soundhaa R. Pandan S. Saavanan A. Agawal Genetc pogammng appoach on evapoaton losses and ts effect on clmate change fo Vapa Basn Intenatonal Jounal of Compute Scence Issues Vol. 8 Issue 5 No Septembe 0. [0] S. Nad M.H. Saaee M.D. Jaz A. Baghe FARS: Fuzzy Ant based Recommende System fo Web Uses Intenatonal Jounal of Compute Scence Issues Vol. 8 Issue Januay 0 [] U. Nanjan R.B.V. Subamanyam V. Khanaa An ffcent System Based On Closed Sequental Pattens fo Web Recommendatons Intenatonal Jounal of Compute Scence Issues Vol. 7 Issue 3 No 4 May 00 [] N. Oen Reexamnng tf.df based nfomaton eteval wth Genetc Pogammng In Poceedngs of Annual confeence of the South Afcan Insttute of Compute Scentsts and Infomaton Technologsts South Afcan Insttute fo Compute Scentsts and Infomaton Technologsts 00 pp.4-34 [3] P. Resnck N. Iacovou M. Suchak P. Begstom and J. Redl Gouplens An open achtectue fo collaboatve flteng of netnews In Poceedngs of ACM CSCW 94 confeence on compute-suppoted coopeatve wok 994 pp [4] B.M. Sawa G. Kayps J.A. Konstan J.T. Redl Applcaton of Dmensonalty Reducton n Recommende System -- A Case Study In Poceedngs of the ACM WebKDD Wokshop 000 [5] P. Symeonds. Content-based Dmensonalty Reducton fo Recommende Systems In Poceedngs of the 3st Annual Confeence of the Gesellschaft fü Klassfkaton 007 pp [6] W.A. Tackett Genetc Pogammng fo Featue Dscovey and Image Dscmnaton In Intenatonal Confeence on Genetc Algothms 993. [7] R.S. Toes A.X. Falcão B. Zhang W. Fan.A. Fox M.A. Gonçalves and P. Calado A new famewok to combne descptos fo content-based mage eteval In Poceedngs of 4th ACM ntenatonal confeence on Infomaton and knowledge management ACM 005 pp Deepa Anand fnshed he M.Tech n Compute Scence fom Jawahalal Nehu Unvesty(JNU) n Delh n 009 and he PhD n Compute Scence fom JNU n 0. She s cuently wokng as an Assstant Pofesso n the Depatment of Compute Scence Chst Unvesty. She has publshed papes n ntenatonal jounals and confeences. He aea of nteest s n Machne Leanng and Computatonal Web Intellgence. Copyght (c) 0 Intenatonal Jounal of Compute Scence Issues. All Rghts Reseved.