Fig. 1. Overall block diagram of the recommendations search in the data-based approach

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1 UDC Y. Stekh, V. Artsbso L Polytechc Ntol Uersty, CAD Deprtmet SOME METHODS IN SOFTWARE DEVELOPMENT RECOMMENDATION SYSTEMS Stekh Y., Artsbso V., 013 Ths rtcle lyzes the crret stte of the models d methods of bldg recommedto systems. The bsc clsses of problems tht sole the recommedto system re hghlghted. The fetres of the method collborte flterg re sho. Deeloped method for clcltg the smlrty coeffcets, tkg to ccot the sprseess of rtgs ectors of goods d people. Key ords: recommeder system, dt mg, collborte flterg, coeffcets of smlrty, ser profles. Проаналізовано сучасний стан моделей і методів побудови рекомендаційних систем. Виділено основні класи задач, які розв язують рекомендаційні системи. Показано особливості застосування методу спільної фільтрації. Розроблено метод розрахунку коефіцієнтів подібності, який враховує розрідженість векторів рейтингів товарів і користувачів. Ключові слова: рекомендаційні системи, інтелектуальний аналіз даних, спільна фільтрація, коефіцієнти подібності, профілі користувачів. Itrodcto Recommedto systems re systems tht operte th prtclr type of formto flterg systemt s recommeded formto elemets tht my be of terest the ser. Typcl recommedtos system recees ser pt s dt ggregtes, d seds them to the teded recpets the form of recommedtos. Ths techology llos sers to sped mmm of tme to fd the rght formto o the Iteret. Recommedto system compres the dt collected from sers d crete lst of tems tht re recommeded to the ser. They re lterte serch lgorthm s help sers qckly fd rtcles d formto tht they old ot fd themseles. Recommedto systems re sed mly to spply the cstomer rel-tme prodcts (flms, books, clothg d serces tht re lkely to be terested t. Especlly, recommedto systems re sed e-commerce. The se of recommedto systems coered recetly o sttory retl trdeformto ceters, serch softre, scetfc rtcles, etc. Ths pplcto s chrcterzed by the proso of dce to sers tomtclly, o the bss of lredy commtted ctos (prchses, exposed rtgs, sts, etc. d tkg feedbck from them (order shops, referrg, etc.. Web recommedto systems (recommedto systems o eb pges re slly mplemeted o Web serers d se the dt obted from the collecto of the resed Web templte (explct dt d ser regstrto formto (explct dt. The most fmos of recommedto systems clde the follog : Amzo.com, Ic. - Amerc compy, the lrgest the orld by troer mog Iteret compes tht sell prodcts d serces ole d oe of the frst ole serces focsed o sles of rel goods of mss demd; eby Ic. - Amerc compy tht prodes serces the res of ole ctos (m feld of ctty, ole shoppgstt pymets, mges the ebste eby.com d ts locl ersos seerl cotres, the compy os PyPl d Eby Eterprse; MoeLes - recommedto system d rtl commty ebste tht recommeds moes to ts sers, recommedtos re proded th regrd profles (rtgs of sers d se collborte flterg lgorthm; Rozetk. - by fr the most poplr ole store electrocs d home 74 L Polytechc Ntol Uersty Istttol Repostory

2 pplces Ukre, represettes of the compy re lble ll regos of Ukre. Recommedto system s oe of the most mportt sectos of dt mg. Methods d tools for bldg recommedto systems Recommedto system s seprte le beg to deelop the lst tety yers. So mke clssfcto of methods d tools for bldg recommedto systems s dffclt. We c dstgsh the follog pproches to bldg recommedto systems: model-bsed; dt-bsed. I pproch bsed o models frst formed descrpte model of ser prefereces, commodtes d the reltoshp betee them, d the formed recommedtos o the bss of the resltg model. The dtge of ths pproch s to he model tht ges more sght geerted recommedtos d reltoshps dt lblty, d the fct tht the formto of recommedtos s dded to to stges: lerg resorce model deferred mode d frly smple clclto bsed o the recommedtos of the exstg model rel tme. Hoeer, these models do ot spport cremetl lerg (the emergece of e dt reqres the coerso of the hole model d mostly sho loer predcto ccrcy th bsed o dt. I dt-bsed pproch the recommedtos re clclted o some smlrty degree ll of the ccmlted dt. These dt re set of ectors of ser rtg d set of ectors of tem rtg. Ths pproch s smpler d shoed hgh ccrcy prctce d hs the dtge of tkg to ccot e dt cremetl (e sers d e prodcts re dded to dtbse d tke to ccot he formg forecsts log th lble. Hoeer, ths pproch s dffclt to clclte terms of tme d memory resorces. Also, ths pproch c ot prode descrpte lyss of exstg ls, to ge more derstdg of the lble dt d expl the forecst. I moder recommedto systems sed sch poerfl compes lke Amzo.com, Yhoo.com, Google.com, eby Ic. mly sed the pproch bsed o the dt. I the pproch bsed o the dt re the follog methods: methods tht focs o the se of ectors of rtgs sers (ser-cetrc; methods tht focs o the se of ectors rkgs tems (tem-cetrc; hybrd methods; mltcrter methods. Geerl block dgrm of dt-bsed pproch shos Fg. 1. Fg. 1. Oerll block dgrm of the recommedtos serch the dt-bsed pproch 75 L Polytechc Ntol Uersty Istttol Repostory

3 Predcto rtg the collborte flterg techqes The bsc method sed dt-bsed pproch s the method of collborte flterg. The ser or tem for hch s forecstg ko rtg, clled the cte ser or cte tem, respectely. The tsk of collborte flterg c be formlted s follos. Let U be set of sers, I set of m tems, R set of m rtgs r ser U d prodct I, S I set of prodcts tht he lredy bee chose by the ser. The prpose of collborte flterg s to predct the rtg p, cte ser for the tem. User s clled cte serf he chose cert tems S Ø. Ths prodct, for hch s forecsts ot ko dce S. Deoted by S set of prodcts tht the ser hs selected, S set of tems tht the ser hs selected. The S set of tems tht sers d he chose. S S r 0 r 0} ; (1 { S S S ; ( m S. (3 Let r r erge rtg of the tem sers d respectely. We deote by T set of sers ho he jotly selected prodcts th the cte ser. The rtg forecst to pproch focses o the se of ectors of ser rtgs s by the follog forml ( rl rl, l l T r r +. (4 l T The rtg forecst to pproch focses o the se of ectors of ser rtgs s by the follog forml r,, N r. (5 N The smmto s oer ll selected prodcts N for ser, - the smlrty betee the tems d. Accrcy rtg forecst s hely depedet o the ccrcy of the clclto of smlrty coeffcets, j. Adtgeosly, smlrty coeffcet s clclted s follos cose of the gle betee ectors (6 or Perso correlto forml (7: r r S, ; (6 r r S,, l S ( r r ( r r S ( r r ( r r. (7 S Prtclrty of tem rtg ectors d ser rtg ectors sers s the fct tht they he lrge mber of zero elemets. Ech ser does ot select ll tems d ech tem s ot selected by ll sers. Adtgeosly, the percetge of o-zero elemets these ectors does ot exceed 10%. The clssc forml for clcltg the smlrty coeffcets do ot clde ths fetre d therefore ge sgfct error the clclto. Let R mx hghest possble rtg the rtg scle ctlog, R m - the loest possble rtg. Let d(,b Eclde dstce betee ectors, d mx (,b the mxmm Eclde dstce to ge set of ectors d S mx (, b ( Rmx Rm. (8 76 L Polytechc Ntol Uersty Istttol Repostory

4 Normlzed Eclde dstce betee the ectors d(, b σ (, b ; (9 d (, b mx ( 0,1] σ (, b. (10 Clclted les of the coeffcet of smlrty for the problem of predctg the rtg ll tke the le coerted to ormlzed Eclde dstce 1 σ (, b R R mx m ( r r S, (11 here m S The trodcto of the coeffcet m llos to tke to ccot the sprseess of rtgs ectors. Itrodcto to the clclto of the smlrty coeffcet Jkrd frther mproes the ccrcy of the clclto S S k j. (1 S S The fl form of the expresso for the clclto of smlrty coeffcets follog S Rmx Rm ( k j f S, r r ; (13 ( r r S Rmx Rm ( k j f S, r r. (14 0,9 + ( r r The proposed pproch to the clclto of the coeffcets of smlrty the problems of collborte flterg llos yo to tke to ccot the cosderble sprsty of these ectors d sgfctly mproe the predcted les for the rtgs. Coclso Ths rtcle lyzes the crret stte of the models d methods of costrcto of recommedto systems. Hghlghts the mjor clsses of the problems tht sole the recommedto system. Sho the fetres the method of collborte flterg. Deeloped method for clcltg the smlrty coeffcets tkg to ccot the sprseess of rtgs ectors of tems d sers. 1. Agrl R. C., Aggr l C. C., Prsd V. V. V., A Tree Pro jecto Algorthm For Geerto of Freqet Itemsets // J. Prll. d Dstrb. Compt., ol. 61, pp , Aggrl C. C., Wolf J. L., W K., Y P. S., H ortg Htc hes Egg : A Ne Grph-Theor etc Ap proch t o Coll borte Flterg. Proc. 5th ACMSIGKDD It. Cof. o Koledge Dscoery d Dt Mg, pp. 01 1, Agr l R., R. Srkt R., Mg S eqetl P tters Pro c. 1 1th I t. Co f. o Dt Egeerg, pp. 3 14, Agrl R., Ime lsk T., Sm A., M g Assocto Rles betee Sets of Items LrgeDtbses. Proc. of the ACM SIGMOD Cof. o Mgemet of Dt, pp , Al dos D. J., Reorgzg Lrge Web S tes.// Amer. M th. Mothly, ol.108, pp.16 7, Atoo G., Hrmele F., A Semtc Web Prmer: MIT Press, edto, Bez- Ytes R. A., Rbero-Neto B.. Moder Iformto Retrel.: Addso-Wesley Logm Pblshg Co., Ic., B lbo c M., Shohm Y., Fb : Cotet-Bsed, Col lborte Recomme dto / / Comm.ACM, ol.40 pp.66 7, B ld P., Fr sco P., Smyth P., Modelg the It eret d the Web: Probbl stc M ethods d Algo rthms: W ley, Ber jee A., Ghosh J., Clc kstrem 77 L Polytechc Ntol Uersty Istttol Repostory

5 Clsterg sg Weghted Logest Commo Sbs eqeces. Pro c. of the Web Mg Workshop t the 1st SIAM Cof. o Dt Mg, pp , Berers-Lee T., Hedler J., Lssl O.. The Semtc Web // S cetfc Am erc o l. 84 p p.34 43, Borg es J., L eee M., D t Mg o f U ser Ngto Ptt ers Proc. I t. W orkshop W EBKDD99 Web Usge Alyss d User Pro flg, pp.31 36, B rke R. Hybrd R ecommeder Systems: Srey d E xpermets // User Modelg d User-Adpted Itercto, ol.1 pp , Cdez D., Heckerm D., Meek C., Smyth P., Whte S. Model-Bsed Cl sterg d V slzto of N gto P tters o Web St e // Dt M. Kol. Dsco., ol.7 pp , Chkrbrt S. Dt Mg for Hypertext: A Ttorl Srey // ACM SIGKDD Explor. Nesl., ol.1 pp.1 11, Cooley R., Mobsher B., J. Srst J. Dt Preprto for M g World W de W eb Bros g Pt ters // Kol. d I formto Sy st., o l.1 pp.5 3, Cosley D., Lrece S., Peock D.M., REFEREE: A Ope Frmeork for Prctcl Testg of Recommeder Systems sg Reserchdex Proc. 8th It. Cof. o Very Lrge Dt Bses, pp.35 46, Demr G.N., Uyr S., S., Güdüz-Ögüdücü S.. Mltobjecte Eoltory Clsterg of Web User Sessos: A Cse Stdy Web Pge Recommedto // Soft Compt., ol.14 pp , Dempster A.P., Lrd N.M., Rb D.B., Mxmm Lkelhood from Icomplete Dt the EM Algorthm // J. Ro yl Sttstcl Socety, Seres B, ol.39 pp.1 38, Deshpde M., Kryps G., Item-Bsed Top-N Rec ommedto Algorthms // ACM Trs. If ormto Syst., ol. pp , Drg A, L P., Scheffer J., Szfro D., Chrter K., Prsos I., FstLSA: A Fst, Ler-Spce, Prllel d Seq etl A lgorthm for Seq ece A lgmet // Alg orthmc, ol.45 pp , Adom cs G., Tzh l A., To rd t he e xt ge erto o f r ecommeder systems: srey o f th e stte-of-the-rt d p ossble e xtesos / / I EEE T rs. Ko ledge d D t Eg eerg, ol. 17, pp , J, Chrstse I. A., Schffo S. Etertmet recommeder systems for grop of se rs // Ex pert Sys tems th Ap plctos, o l. 38, pp , Tg X., Ze g Q. Keyord c lsterg for s er terest pro flg r efemet th pp er recommeder s ystem / / Jo rl o f Systems d So ftre, o l., pp , Segs T. A FPGA mp lemetto of rel-tme K-mes cl sterg fo r color mg es / / Rel Tm e Imge Proce ssg, o l., pp , Stekh Y., Lobr M., F sl M.E. Sr deh, Dombro M., Art sbso V. Re serch d de elopmet of methods d lg orthms o-herrchcl c lsterg," Pr oc. o f the XI th Itertol Cof erece CADSM, L-Poly, 011, pp L obr M., Stekh Y., Kerytskyy A., F sl M.E. Srdeh Some tr eds koledge dsc oery d d t m g Proc. of the IVt h Ite rtol Coferece MEMSTECH, L-Poly, 008, pp L Polytechc Ntol Uersty Istttol Repostory

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