ICBCF: One Item-Classification-Based Collaborative Filtering Algorithm

Transcription

1 ICBCF: One Item-Clssfcton-Bsed Collbotve Flteng Algothm Zle SUN Nnlong LUO We KUANG Compute nd Infomton Mngement Cente Tsnghu Unvesty Beng Chn Compute nd Infomton Mngement Cente Tsnghu Unvesty Beng Chn Compute nd Infomton Mngement Cente Tsnghu Unvesty Beng Chn nd the spsty of dt dstbuton [6] hven t been solved snce the ppence of ecommendton system. Abstct Wth the development of pesonlzed ecommendton ecommendton lgothms usully need to consde the specfc fetue of the system so s to obtn moe nfomton nd get bette esult. To mpove the egul collbotve flteng lgothms whch s neffcency nd less concened bout tem clssfcton ths ppe poposes new tem-clssfcton-bsed lgothm. It poposes the concept of Use Inteest Vecto n ode to pesent uses nteests nd tng tendency bette nd then coect the clssfcton nfomton of ll the tems. We beleve ths lgothm whch hs bette ccucy nd lowe computton complexty n expements s woth populzton nd becomng new esech decton of collbotve flteng lgothm. Ths ppe nlyzes the met nd defect of thee tdtonl tem-bsed ecommendton lgothms nd lso poposes new tem-clssfcton-bsed collbotve flteng lgothm. Not only t nhets the dvntges of tdtonl collbotve flteng lgothms: consdeton of othe uses tngs when sevng specfc one but t sgnfcntly nceses the effcency nd solve the cold stt poblem nd the scsty of dt dstbuton well whch s suppoted by the good esult of the expements. II. Keywods-tem clssfcton; collbotve flteng; use nteest vecto; tem vecto; mtchng degee I. THE SURVEY OF CLASSICAL ALGORITHMS A. Item-bsed collbotve flteng lgothm[] Poposed by Swl(00) s the best known tem-bsed collbotve flteng lgothm. It ssumes tht uses tngs of sml tems e lso sml nd pedcts use s te of cuent tem ccodng to the tems he o she ledy ted. INTRODUCTION Wth the gowth of E-Commece s well s the se of Busness-to-Custome (BC) pesonlzed ecommendton hs become n mpotnt fled n the development of web pplctons n ths new ge. Futhemoe n some felds lke musc move nd book ecommendton hs lso been wdely used s n mpotnt technology. Amzon s n outstndng exmple: the collbotve ecommendton system fo ts buyes povdes get mpetus fo ts development nd bngs lot of pofts []. Move evluton webste Mtme.com n chn lso contns sml move ecommend functon whch shows uses sevel moves tht s sml wth the one they e bowsng. It dels wth smlty mesuement of the tems bsed on the followng hypothess: f the exsts tngs of one tem e sml the comng tngs pobbly e sml too. In the ppoch to mesue the smlty between two tems Peson coelton coeffcent s used hee [9] [0]: w = Cuently the pesonlzed ecommendton technology fces lot of chnges. Fstly due to the gowth of dt some clssc lgothms cnnot meet the equement of el system. Fo exmple n Use-Bsed lgothm [] nd Item-Bsed [3] lgothm (both collbotve flteng lgothms []) mssve vecto clcultons consume too much tme to be effcent. Slope One lgothm [4] poposed by Dnel Leme t 005 eplces the vecto clcultons wth smple lne clcultons menwhle mntns the ccucy. Secondly the scenes of pesonlzed ecommendton e becomng so vous tht the tdtonl lgothms whch e usully desgned fo one stuton lck flexblty nd opeblty n el pplcton. And thdly the ksome cold stt poblem [5] T T T T ( )( ) ( ) T T ( ) ( ) whee s the tng of tem I gven by use U whle s the vege tng of tem I. T s the set of the uses who hve ted tem I. Then t sots the tems tht e sml to the tget tem by smlty nd pck up mx k tems s the neghbos of the tget tem [7]. In the end t pedct cuent use s te of the tget tem by hs tngs of the neghbos s shown n Fomul -: //$ IEEE 86

2 ( w ) R ( ) χ = w ( ) R χ ( ) whee R s the set of the tems ted byu nd w s Peson coelton coeffcent tht s clculted bove. Fomul - shows tht the mssve vecto clculton n smlty mesuement sgnfcntly ffect the effcency of the lgothm. Also n the te pedctng pocess tht follows the spsty of dt hs get effect on the ccucy of the pedcton whch mens tht chnce would ply n mpotnt ole n clculton f the neghbo tems she smll numbe of uses. Fo the vew of effcency Item-bsed lgothms e moe sutble fo tem ntensve ecommendton system snce the less tems cn led to less computton nd speed up the system; menwhle moe uses my led to dt spsty nd then the low numbe of uses shng by tems whch estcts the pplcton of these lgothms. B. Slope One Algothm[4] As dt gows the tme cost of tdtonl collbotve flteng lgothm nceses pdly. To solve the poblem A new tem-bsed collbotve flteng lgothm nmed Slope One ws poposed t 005. Slope One s lso bsed on uses tngs ts pedcton fomul s lke f(x)=x+b. Comped wth tdtonl tem-bsed lgothm the mo dvntge of Slop One s ts smplcty nd ccucy. The mo mpovement of Slope One s the smlty mesuement t mesues the elton between two tems wth two ndctos: the numbe of uses tht te both tems nd the dffeence of the tngs. The fomul s shown s follows: dev = T( χ) T( χ) c c = cd( T( χ) T( χ)) ( 3) ( 4) whee Cd(S) stnds fo the numbe of elements n the set c s the numbe of uses tht te both I nd I dev s the tng dffeence between I nd I. the sme s the vege vlue of the tngs gven by fome uses. The fomul s shown s follows: = ( 6). Ths lgothm hs no contbuton on pesonlzton but t s vey smple nd even moe ccute thn the clssc lgothms n dt spsty stuton. So t cn be slghtly mpoved nd ppled n mny el ecommendton systems. III. ITEM-CLASSIFICATION-BASED COLLABORATIVE FILTERING ALGORITHM(ICBCF) Accodng to bove nlyss ll these ecommendton lgothms hve the own chctestcs but lso hve wekness n pctcl pplctons. Fo exmple when dt e spse both Item-Bsed lgothm nd Slope One lgothm wll fl t smlty mesuement. Item-vege cn hndle dt spsty nd cold stt poblem well but stll doesn t hve good oppotunty due to ts lck of pesonlzton. Tke el ecommendton systems s exmples n move ecommendton webstes uses nteests on one move bsed on ts tngs on one hnd nd ts gene on the othe hnd sme s tht n musc stes nd book ones. In ll these cses the mpotnce of types (o genes) of the tems s usully gnoed. Though the tng pocedue my hve some gudnce uses needs o hobbes my lso stong gudnce whch mples tht hgh te my not becuse use lkes the tem but becuse he o she my be moe wllng to ccept the tems of cetn type. If thee e k popetes of the tem n system nmed s k then one tem I cn be epesented by k-dmensonl vecto S =( k ). Set = f the popety of tem s tue else set =0. Now we poposed ICBCF collbotve flteng lgothm bsed on tem clssfcton whch tkes tems nd ctegoes tht uses hve bowsed nto consdeton nd genetes the ecommendton esults by evlutng use s fvo of dffeent ctegoes. The pocess s shown s follows. The fnl pedcton of Slope One lgothm s clculted s: R = { R cd( T( χ) T( χ)) 0} c ( dev + ) R = c R. ( 5) Snce Slope One uses lne clculton nsted of vecto clculton n these two stges (smlty mesuement nd te pedctng) t hs get mpovement on effcency. C. Item-Avege Algothm Item-vege lgothm s the smplest ecommendton lgothm t pedcts the tngs of cuent use on one tem s Fgue It hs few steps: ICBCF Algothm Flowcht 87

3 () Clculte the nteest vecto of use. Ths clculton cn be futhe dvded nto fou steps:. Set nteest vecto u fo ech use U the demenson of whch s s sme s the numbe of ctegoes n the system. The ntl weght vlue of evey component s 0. b. If tem belongs to one ctegoy then we dd some weght vlue to the coespondng component of the nteest vectos of the uses tht hve ted ths tem. The weght vlue whch s geneted by exponentl functon wll se pdly s the tng of the tem gows. Snce use s te tself stnds fo hs/he nteests low theshold cn guntee tht evey te contbutes to the nteest vecto. c. Set ssstnt vecto to ecod the tem count of ech ctegoy tht hs been ted nd we cn get uses pefeenc on ech ctegoy when the nteest vecto s dvded by ths ssstnt vecto. d. Nomlze the nteest vecto to keep ts nk s. Clcultng fomuls of nteest vecto e shown s follows: ( ) ρ R u = mx( S S ) S = S R fo to k u = u / S u = u / u (3-) The nteest mples two fetues of uses: the ctegoes tht use pefes nd the ctegoes tht use tes hgh. () Clculte the tem vecto. Item vecto epesents the nteest dstbuton of uses who lke ths tem not the tem o the ctegoy themselves. Thee e 3 mn steps of the clculton:. Set n ntl vecto fo ech tem I the dmenson of whch equls wth the ctegoy count k n the system. The ntl weght vlue of evey component s 0. b. Plus use s nteest vecto nto tem vecto evey tme the tem s ted. c. Nomlze the tem vecto to nsue the nk s. Clcultng fomul of tem vecto: = u T = / (3-) Snce the ddton opeton of use nteest vecto nd tem vecto s lne the lgothm cn updte dt ncementlly when new vst ecod s ceted whch s get optmzton on effcency. (3) Pedct the tngs. Fstly we defne mtchng degee between use tem I s follows: U nd m = u (3-3) whee u stnds fo the nteest vecto of use U nd stnds fo the vecto of tem I. Secondly we set vble nmed use pefeence whch equls to the dffeence of m nd the vege mtchng degee m of the whole system. It cn epesent the pefeence of use U on tem I. Then we weght ths vlue wth coeffcent ρ nd dd the esult wth vege tng of use U. The lst two steps e shown n the followng fomul: p( u ) = ( m m) ρ + (3-4) whee ρ ses dectly wth the quotent of the vege vnce of use tngs nd the vege use pefeence of the system. IV. ANALYSIS OF COMPLEXITY A. Item-bsed collbotve flteng lgothm Smlty mesuement s the most complex pt of tembsed lgothm. If thee e n tems n the system t would tke n(n-)/ clcultons. Snce evey sngle clculton hs tme complexty of O(m) n whch m stnds fo the numbe of uses the ovell tme complexty of the lgothm s Omn ( ). Addtonlly the vege-cse tme complexty s Okn ( ) n whch k s the vege vst count of ll tems. Spce complexty of the lgothm s On ( ) most spce s used to stoe the smlty dt of tems. B. Slope One lgothm It lso tkes Slope One lgothm n(n-)/ clcultons to mesue the eltons between tems. The mesuement ncludes countng the numbe of uses tht te both the two tems nd clcultng the dffeence of the tngs. Snce the complexty of the mesuement s lso O(m) the ovell tme complexty of the lgothm s Omn ( ). Sme s tem-bsed lgothm the vege-cse tme complexty s Okn ( ) nd spce complexty s On ( ) too. 88

4 C. Item-Avege Algothm Ths lgothm s so smple tht both spce nd tme complexty s Omn ( ) whle the vege-cse complexty s s low s Okn ( ) n whch k s lso the vege vst count of ll tems. D. ICBCF lgothm Tme complexty of use nteest vecto clculton nd tem vecto clculton e both Okmn ( ) so the ovell tme complexty s O(mn) n whch k stnds fo the numbe of tem ctegoes. Actully vege tme complexty of use nteest vecto clculton s Okkm ( ) whee k stnds fo the vege vst count of ll uses nd vege complexty of tem vecto clculton s Okkn ( ) so the vege-cse tme complexty of ths lgothm s O(m+n). Stted thus ICBCF lgothm pefoms bette thn the fst two on tme complexty. Sme s tem-vege lgothm spce complexty of the lgothm s lso O(mn). The vege-cse spce complexty s O(m+n). V. EXPERIMENT A. Expement envonment We use Movelens [8] dt set n ou expement whch s publshed by GoupLens Unvesty of Mnnesot nd contns tngs evolve 943 uses nd 68 tems. Movelens s the elest ecommendton system. It hs done get contbuton to the development of collbotve flteng lgothm. And n ou cse t povdes pope pplcton sceno fo the ICBCF lgothm. In the dt set ll moves e gouped unde 8 ctegoes such s cton dventue nmton chlden nd comedy nd so on. Evey move belongs to one o sevel ctegoes so ICBCF lgothm cn buld model fo use nteest vectos nd tem vectos dectly nd then mke fnl pedcton. We ndomly dvde the dt set nto 5 pts 80% of them e used s tnng dt of use nteest vecto nd tem vecto nd 0% left e test dt of tng the pedcted tngs of whch e used to udge whethe the lgothm s ccute. At fst ou expements evlute the ccucy of lgothms by the genel stndd of collbotve flteng lgothms: MAE (Men Absolute Eo) []. The fomul shows s follows: MAE = ( ) u p( u) (5-) u χ cd nge χ stnds fo the test dt set nd (u) stnds fo use-tem p n the dt set. u stnds fo the el tng of use U hs fo the tem I whch s hdden n χ nd p( u) s the fnl pedcton of the lgothm. nge s the tng nge of the dt set fo exmple the tng nge of ecommendton wth mxmum tng of 5 s 4. Ou expements would clculte the MAE vlue unde dffeent spsty. Clcultng fomul of spsty s: cd( use) cd( tem) Spsty = (5-) cd( tng) In the fomul cd(use) stnds fo the use numbe of the system cd(tem) stnds fo the tem numbe nd cd(tng) stnds fo the tng numbe. Menwhle ccucy of tng s genelly n nvsble ndcton the ccucy of ecommendton s moe mpotnt. In ode to mesue t pecson n mpotnt ndex n nfomton etevl s used hee [] nd the clcultng fomul s: N s pecson = (5-3) N N s stnds fo the numbe of ll the tems ecommended to the use N s stnds fo the numbe of the tem tht use lkes n ll the tems ecommended. B. Expement Result Tble 5- shows the MAE esults of these lgothms the expements e fnshed n two condtons: the ognl whole dt set nd 0 tmes dluted dt set. Fgue MAE Compson on Dtset wth Dffeent Spsty It shows tht when the dt set s eltvely dense (spsty of 6.5%) the pedctons mde by Slope One e most ccute. But when the dt set s spse (spsty of 0.6%) the Item-Avege lgothm nd ICBCF lgothm e bette nd moe stble. Consdeng the fct tht the scctes of most el ecommendton systems e lowe thn % we mesue the ccuces of these lgothms usng 0 tmes dluted dt set. Then we septely tke top 5% 0% nd 5% tems s the ecommendton tems ccodng the pedcton tngs gven by ech lgothm nd clculte the pobblty of uses lkng them. If we conclude tht use s nteest on one tem when ts tng eches 5 the esults of pecson e shown n Fgue 3: s 89

5 nd mpoves the tem vecto mechnsm by usng these use nteest vectos. Then t genetes the fnl ecommendtons by udgng the mtchng degees between use nteest vectos nd tem vectos. The mo contbutons of ths lgothm ncludes povdng n effectve ecommendton lgothm fo ecommendton systems tht hve ctegoy nfomton nd sttng new decton of collbotve flteng lgothm esech. As we concluded n scenos lke move nd book tht hs sgnfcnt dffeences between tem ctegoes nd specl needs of uses the ctegoy nfomton should be ctcl fct to consde n futue ecommendton mechnsm. Fgue 3 Pecson Compson of fou lgothm wth Dffeent Popotons of Recommendton() If we conclude tht use s nteest on one tem when ts tng equls to o lge thn 4 the esults of pecson e shown s follows: Fgue 4 Pecson Compson of fou lgothm wth Dffeent Popotons of Recommendton() The esults show tht ICBCF lgothm s most ccute unde dffeent ccumstnces. Slope One cn ecommend the esults tht equl to o lge thn 4 well. Item-bsed nd temvege lgothm do not pefom well especlly the temvege lgothm whch fequently doesn t put good tems on ts best poston nd cuts them off the fnl esults s not pplcble fo el ecommendton system. VI. CONCLUSION To mpove the effcency of clssc collbotve flteng lgothms nd tke the ctegoy nfomton of tems nto consdeton we popose new tem-clssfcton-bsed collbotve flteng lgothm: ICBCF. It combnes uses nteest nd the ctegoes of tems nto use nteest vectos ACKNOWLEDGMENT The wok ws suppoted by the Ntonl Hgh Technology Resech nd Development Pogm of Chn (863 Pogm) (No. 008AA0Z3). REFERENCES [] Geg Lnden Bent Smth Jeemy Yok Amzon.com Recommendtons: Item-to-Item Collbotve Flteng IEEE Intenet Computng vol. 7 no. pp [] B. Sw G. Kyps J. Konstn nd J. Redl Item-Bsed Collbotve Flteng Recommendton Algothms Poc. 0th Intl WWW Conf 00 [3] J.A. Konstn B.N. Mlle D. Mltz J.L. Helocke L.R. Godon nd J. Redl GoupLens: Applyng Collbotve Flteng to Usenet News Comm. ACM vol. 40 no. 3 pp [4] Leme D McLchln A Slope One Pedctos fo onlne tng-bsed collbotve flteng. In: SIAM Dt Mnng (SDM05) Newpot Bech Clfon Apl [5] A.I. Schen A. Popescul L.H. Ung nd D.M. Pennock Methods nd Metcs fo Cold-Stt Recommendtons Poc. 5th Ann. Intl ACM SIGIR Conf. 00. [6] M. Blbnovc nd Y. Shohm Fb: Content-Bsed Collbotve Recommendton Comm. ACM vol. 40 no. 3 pp [7] M. Deshpnde nd G. Kyps Item-Bsed Top-N Recommendton Algothms ACM Tns. Infomton Systems vol. no. pp [8] B.N. Mlle I. Albet S.K. Lm J.A. Konstn nd J. Redl MoveLens Unplugged: Expeences wth n Occsonlly Connected Recommende System Poc. Intl Conf. Intellgent Use Intefces 003. [9] P. Resnck N. Ikovou M. Sushk P. Begstom nd J. Redl GoupLens: An Open Achtectue fo Collbotve Flteng of Netnews Poc. 994 Compute Suppoted Coopetve Wok Conf. 994.G. Slton Automtc Text Pocessng. Addson-Wesley 989. [0] U. Shdnnd nd P. Mes Socl Infomton Flteng: Algothms fo Automtng Wod of Mouth Poc. Conf. Humn Fctos n Computng Systems 995. [] D. Goldbeg D. Nchols B.M. Ok nd D. Tey Usng Collbotve Flteng to Weve n Infomton Tpesty Comm. ACM vol. 35 no. pp [] J.L. Helocke J.A. Konstn A. Boches nd J. Redl An Algothmc Fmewok fo Pefomng Collbotve Flteng Poc. nd Ann. Intl ACM SIGIR Conf. Resech nd Development n Infomton Retevl (SIGIR 99)