Leveraging User-specified Metadata to Personalize Image Search

Transcription

1 Chaer I Leveran User-secfed Meadaa o Personale Imae Search Krsna Lerman USC Informaon Scences Inse, USA Anon Planrasochok USC Informaon Scences Inse, USA ABSTRACT The socal meda ses, sch as Flckr and del.co.s, allow sers o load conen and annoae wh descrve labels known as as, jon secal-neres ros, ec. We beleve ser-eneraed meadaa exresses ser s ases and neress and can be sed o ersonale nformaon o an ndvdal ser. Secfcally, we descrbe a machne learnn mehod ha analyes a cors of aed conen o fnd hdden ocs. We hen hese learned ocs o selec conen ha maches ser s neress. We emrcally valdaed hs aroach on he socal hoo-sharn se Flckr, whch allows sers o annoae maes wh freely chosen as and o search for maes labeled wh a ceran a. We se meadaa assocaed wh maes aed wh an ambos qery erm o denfy ocs corresondn o dfferen senses of he erm, and hen ersonale resls of mae search by dslayn o he ser only hose maes ha are of neres o her. INTRODUCTION The rse of he Socal Web nderscores a fndamenal ransformaon of he Web. Raher han smly searchn for, and assvely consmn, nformaon, sers of blos, wks and socal meda ses lke del.co.s, Flckr and d, are crean, evalan, and dsrbn nformaon. In he rocess of sn hese ses, sers are eneran no only conen ha cold be of neres o oher sers, b also a lare qany of meadaa n he form of as and rans, whch can be sed o mrove Web search and ersonalaon. Web ersonalaon refers o he rocess of csomn Web exerence o an ndvdal ser Mobasher, Personalaon s sed by onlne sores o recommend relevan rodcs o a arclar ser and o csome a ser s shon exerence. I s sed by adversn frms o are ads o a arclar ser. Search ersonalaon has also been sded as a way o mrove he qaly of Web search Ma, 2007 by dsamban qery erms based on ser s browsn hsory or by elmnan rrelevan docmens from search resls. Personaln mae search s an esecally challenn roblem, becase, nlke docmens, maes enerally conan lle ex ha can be sed for dsamban erms. Consder, for examle, a ser searchn for hoos of jaars. Shold he sysem rern maes of lxry cars or soed felnes o he ser? In hs conex, ersonalaon can hel dsambae qery keywords sed n mae search or o weed o rrelevan maes from search resls. Therefore, f a ser s neresed n wldlfe, he sysem wll show her maes of he redaory ca of Soh Amerca and no of an aomoble. In hs chaer we exlore a novel sorce of evdence ser-eneraed meadaa ha can be sed o ersonale mae search resls. We erform a case sdy of he echnqe on he socal hoo- 1

2 sharn se Flckr, whch allows sers o load maes and label hem wh freely-chosen keywords, known as as. Tas are mean o hel sers orane conen and make searchable by hemselves and ohers. In addon o descrbn and caeorn maes, as also care ser s hoorahy neress. We se a machne learnn mehod o fnd ocs of a lare cors of aed maes rerned by mae search on Flckr. We hen se he learned ocs o mach maes o an ndvdal ser s neress. Ths aears o be a romsn mehod for mrovn he qaly of mae search resls. BACKGROUND Tradonally, ersonalaon echnqes fall n one of wo caeores: collaborave-flern or roflebased. The frs, collaborave flern Breese, 1998; Schafer, 2007, areaes onons of many sers o recommend new ems o lke-mnded sers. In hese sysems, sers are asked o rae ems on a nversal scale. The sysem hen analyses rans from many sers o denfy hose sharn smlar onons abo ems and recommends new ems ha hese sers lked. Neflx ses collaborave flern o recommend moves o s sbscrbers. Amaon ses a smlar echnoloy o dslay oher rodcs ha sers who rchased a ven rodc were also neresed n. Snce sers are asked o rae ems on a nversal scale, he qesons of how o desn he ran sysem and how o elc hh qaly rans from sers are very moran. Dese he early concern ha sers lack ncenves for makn recommendaons and, herefore, wll be relcan o make he exra effor, here s new evdence Schafer, 2007 ha hs does no aear o be he case. I aears ha, a he very leas, sers fnd vale n a collaborave ran sysem as an exenson of her memory. The second class of ersonalaon sysems ses a rofle of ser's neress o are ems for ser's aenon. The rofle can be creaed exlcly by he ser Ma, 2007, or mned from daa abo ser s behavor. Examles of he laer nclde daa abo ser s Web browsn Mobasher, 2000 and rchasn Arawal, 1994 behavor. One roblem wh hs aroach s ha s me-consmn for sers o kee her exlc rofles crren. Anoher roblem s ha whle daa mnn mehods have roven effecve and commercally sccessfl, n mos cases hey se roreary daa, whch s no easly accessble o researchers. Machne learnn has layed an ncreasnly moran role n ersonalaon. Poescl, 2001 roosed a robablsc enerave model ha descrbes co-occrrences of sers and ems of neres. In arclar, he model assmes a ser eneraes her ocs of neres; hen he ocs enerae docmens and words n hose docmens f he ser refers hose docmens. The ahor-oc model Rosen-Zv, 2004 s also sed o fnd laen ocs n a collecon of docmens and ro docmens accordn o oc. If a ser refers one docmen or oc, hs mehod can be sed o recommend oher relevan docmens. These models, however, do no carry any nformaon abo ndvdal sers, her ases and neress. However, a recen work hs area descrbed a mxre model for collaborave flern ha akes no accon sers' nrnsc references abo ems Jn, In hs model, em ran s eneraed from boh he em ye and ser's ndvdal reference for ha ye. Invely, lke-mnded sers rovde smlar rans on smlar yes of ems e.., move enres. When redcn a ran of an em for a ceran ser, he ser's revos rans on oher ems wll be sed o nfer a lke-mnded ro of sers, and hen he common ran of ha ro s sed n he redcon. Ths ye of model can concevably be adaed o socal meadaa and be sed o ersonale resls of mae search. LEVERAGING USER-GENERATED METADATA FOR PERSONALIZATION The Web 2.0 has creaed an exloson no only n ser-eneraed conen, b also n ser-eneraed meadaa. Ths daa abo daa s exressed n a nmber of ways on he Socal Web ses: hroh as descrve labels chosen by he ser, rans, commens and dscsson abo s, ems ha sers mark as her favore, and hroh he socal neworks sers creae and he secal-neres ros hey arcae n. Ths meadaa rovdes a wealh of nformaon abo ndvdal ser s ases, references and neress. Socal Web ses crrenly don make mch se of hs daa, exce erhas o are adversemen o ndvdal sers or ros. However, hs daa has he oenal o ransform how sers 2

3 dscover, rocess and se nformaon. For examle, Web browsn and search can be ned o an ndvdal ser based on hs or her exressed neress. Raher han reqrn he ser dsambae qery erms, e.., hroh qery exanson, n order o mrove resls of Web search, a ersonalaon sysem wold nfer a ser s meann based on he rch race of conen and meadaa he ser has creaed. Sch meadaa cold also fler he vas sream of new conen creaed daly on he Web and recommend o he ser only ha conen he ser wold fnd relevan or neresn. Personalaon, recommendaon and flern are js some of he alcaons of ser-eneraed meadaa ha have recenly been exlored by researchers. Isses, Conroverses, Problems In hs chaer we focs on as, alhoh he analyss can be easly exanded o nclde oher yes of meadaa, ncldn socal neworks Lerman e al., Tas are freely-chosen keywords sers assocae wh conen. Tan was nrodced as a means for sers o orane her own conen n order o faclae searchn and browsn for relevan nformaon. The dsnshn feare of an sysems s ha hey se an nconrolled vocablary, and ha he ser s free o hhlh any one of he objec's roeres. From an alorhmc on of vew, an sysems offer many challenes ha arse when sers ry o aach semancs o objecs hroh keywords Golder, These challenes are homonymy he same a may have dfferen meanns, olysemy a has mlle relaed meanns, synonymy mlle as have he same meann, and basc level varaon sers descrbe an em by erms a dfferen levels of secfcy, e.., beale vs do. Dese hese challenes, an s a lh weh, flexble caeoraon sysem. The rown amon of aed conen rovdes evdence ha sers are adon an on Flckr Marlow, 2006, Del.co.s and oher collaborave an sysems. In a small case sdy we show how as on he socal hoo-sharn se Flckr can be sed o ersonale resls of mae search. 3

4 Fre 1: Screen sho of an mae ae of Flckr ser Tambako he Jaar shown he mae and he as he aached o he mae. Flckr consss of a collecon of nerlnked ser, hoo, a and ro aes. A ycal Flckr hoo ae, shown n Fre 1, rovdes a varey of nformaon abo he mae: who loaded and when, wha ros has been sbmed o, s as, who commened on he mae and when, how many mes he mae was vewed or bookmarked as a favore. The ser calln hmself ser s may reveal her ender n her rofle, as hs ser has chosen o do Tambako he Jaar osed a hoorah of a swmmn er a a Swss oo. To he rh of he mae s a ls of keywords, as, he ser has assocaed wh he mae. 1 These as nclde er, b ca, wld ca, anhera rs, and felne, all sefl erms for descrbn hs arclar sense of he word er. Clckn on a ser's name brns ha ser's hoo sream, whch shows he laes hoos he loaded, he maes he marked as favore, and hs rofle, whch ves nformaon abo he ser, ncldn a ls of hs socal nework conacs and ros he belon o. Clckn on he a shows ser's maes ha have been aed wh ha keyword, or all blc maes ha have been smlarly aed. 4

5 Fre 2: Ta clod vew of he as he owner of he mae n F. 1 sed o annoae hs maes. The ber he fon, he more freqenly ha a was sed by he ser. Informaon abo a ser s hoorahy ases and neress s conaned n he rch meadaa he creaes n hs everyday acves on Flckr. He exresses hese neress hroh he conacs he adds o hs socal neworks, he ros he jons, he maes of oher hoorahers he marks as hs favore or commens on, as well as hroh as he adds o hs own maes. Fre 2 shows a a clod vew of he as ha Tamboko he Jaar sed o annoae hs maes on Flckr. The ber he fon, he more freqenly ha keyword was sed. These as clearly show ha he ser s neresed n wldlfe bca, ca, lon, cheeah, er, re, wldca and nare clods, monans hoorahy. They also show ha he shoos wh a Nkon nkon, d300 and has raveled exensvely n Eroe swerland, ermany, france and ars of Afrca kenya. These neress are frher refleced n he ros he ser joned, whch are lsed on hs rofle ae, ha nclde sch ad-hoc ros as Horns and Anlers, Exoc cas, Cheeah Collecon, and many ohers. In hs work, we vew ro names js as we rea as hemselves. In fac, ro names can be vewed as blcly areed-on as. Flckr allows sers o search for hoos ha conan secfed keywords n her descrons ncldn les or as. A ser can search all blc hoos, or resrc he search o hoos from her conacs, her own hoos, or hoos she marked as her favore. Search resls are by defal dslayed n reverse chronolocal order of ben loaded, wh he mos recen maes on o. Anoher oon s o dslay maes by her neresnness, 2 wh he mos neresn maes on o. Sose a ser s neresed n wldlfe hoorahy and wans o see maes of ers on Flckr. As of Seember 9, 2008, he search of all blc maes aed wh he keyword er rerned over 170,000 resls. When arraned by neresnness, he frs few aes of resls conan maes of ers, b also many rrelevan maes of cas, kds, berfles, flowers, and olf, as shown n Fre 3, and also sharks and screenshos of Mac OS X comer sysem. 5

6 Fre 3: Resls of mae search on Flckr for maes aed wh er We assme ha when a search erm s ambos, he sense ha he ser has n mnd s relaed o hs or her neress. A wldlfe hooraher searchn for er maes s robably no neresed n hoorahs of chldren wh face an. Smlarly, a chld hooraher searchn for cres of newborns s mos lkely neresed n maes of hman babes, no kens or er cbs. In hs chaer we show ha we can mrove he relevance of mae search by ersonaln mae search resls on Flckr. We se ser-eneraed meadaa, n he form of as and he ros, for hs rose. Inferrn ersonal neress from as, however, s roblemac, snce hs daa s sarse few as er mae and nosy dosyncrac vocablary se, synonyms, ec. Machne learnn mehods, whch ry o fnd sascal correlaons n he daa, drecly address some of hese challenes. In he secon below, we descrbe a machne learnn-based mehod ha exlos nformaon conaned n ser-eneraed meadaa, secfcally as, o ersonale mae search resls o an ndvdal ser. 6

7 Probablsc Model for Ta-based Personalaon We olne a robablsc model ha akes advanae of he maes' a and ro nformaon o dscover laen ocs conaned n a se of maes. If he daase s a resl of a search for maes ha have been aed wh he qery erm, he ocs corresond o dfferen senses of he qery erm. The sers' neress can smlarly be descrbed by collecons of as hey sed o descrbe her own maes. The laen ocs fond by he model can be sed o ersonale search resls by fndn maes on ocs ha are of neres o he ser. We consder for yes of enes n he model: a se of sers U={ 1,..., n }, a se of maes or hoos I={ 1,..., m }, a se of as T={ 1,..., o }, and a se of ros G={ 1,..., }. A hoo x osed by ser mae owner x s descrbed by a se of as { x1, x2,...} and sbmed o several ros { x1, x2,...}. Ths os cold be vewed as a le < x, x, { x1, x2,...},{ x1, x2,...}>. We assme ha here are n sers, m osed hoos and ros n Flckr. Meanwhle, he vocablary se of as s q. In order o fler maes rereved by Flckr n resonse o a search and ersonale hem for a ser, we come he condonal robably, ha descrbes he robably ha he hoo s relevan o based on her neress. Imaes wh hh enoh are hen resened o he ser as relevan maes. As menoned earler, sers choose as from an nconrolled vocablary accordn o her syles and neress. Imaes of he same sbjec cold be aed wh dfferen keywords alhoh hey have smlar meann. Meanwhle, he same keyword cold be sed o a maes of dfferen sbjecs. In addon, a arclar a freqenly sed by one ser may have a dfferen meann o anoher ser. Probablsc models offer a mechansm for addressn he sses of synonymy, homonymy and a sarseness ha arse n an sysems. We se a robablsc oc model Rosen-Zv, 2004 o model ser's mae osn behavor. As n a ycal robablsc oc model, ocs are hdden varables, reresenn knowlede caeores. In or case, ocs are eqvalen o mae owner's neress. The rocess of hoo osn by a arclar ser cold be descrbed as a sochasc rocess: - User decdes o os a hoo. - Based on ser 's neress and he sbjec of he hoo, a se of ocs are chosen. - Ta s hen seleced based on he se of ocs chosen n he revos sae. - In case ha decdes o exose her hoo o some ros, a ro s hen seleced accordn o he chosen ocs. Fre 4. Grahcal reresenaon for model-based nformaon flern. U, T, G and Z denoe varables User, Ta, Gro, and Toc resecvely. N reresens a nmber of a occrrences for a one hoo by he hoo owner; D reresens a nmber of all hoos on Flckr. Meanwhle, N denoes a nmber of ros for a arclar hoo. 7

8 8 The rocess s deced n a rahcal form n Fre 4. We do no rea he mae as a varable n he model b vew as a co-occrrence of a ser, a se of as and a se of ros. From he rocess descrbed above, we can reresen he jon robably of ser, a and ro for a arclar hoo as n n n k n k G T,, n and n are he nmbers of all ossble as and ros resecvely n he daa se. Meanwhle, n and n ac as ndcaor fncons: n =1 f an mae s aed wh a ; oherwse, s 0. Smlarly, n =1 f an mae s sbmed o ro ; oherwse, s 0. k s he redefned nmber of ocs. Noe ha s srahforward o exclde hoo's ro nformaon from he above eqaon smly by omn he erms relevan o. In order o esmae arameers,, and, we defne a lo lkelhood L=lo, whch measres how he esmaed arameers f he observed daa, n or case all he hoos n he daase. We se he EM alorhm Demser, 1977 o erae beween arameer esmaes nl he lo lkelhood for all arameer vales converes. L s sed as an objecve fncon o esmae all arameers. In he execaon se E-se, he jon robably of he hdden varable Z ven all observaons s comed from he follown eqaons:,, L canno be maxmed easly, snce he smmaon over he hdden varable Z aears nsde he loarhm. We nsead maxme he execed comlee daa lo-lkelhood over he hdden varable, E[L c ], whch s defned as m m m c n n L E lo, lo, lo ] [ Snce he erm lo s no relevan o arameers and can be comed drecly from he observed daa, we dscard hs erm from he execed comlee daa lolkelhood. Wh normalaon consrans on all arameers, Larane mllers,, are added o he execed lo lkelhood, yeldn he follown eqaon c L E H ] [ We maxme H wh resec o,, and, and hen elmnae he Larane mllers o oban he follown eqaons for he maxmaon se:

9 n, n, n, n, The alorhm eraes beween E and M se nl he lo lkelhood for all arameer vales converes. Addonal deals abo model dervaon and nference mehod can be fond n Lerman, We can se he arameers nferred from he daase o fnd he maes mos relevan o he neress of a arclar ser. We do so by comn he condonal robably :, T, G, where s he owner of mae n he daa se, and T and G are, resecvely, he se of all he as and ros for he mae. We reresen he neress of ser as an areae of he as she sed n he as for an her own maes. Ths nformaon s sed o o aroxmae : ' n ' where n = s a freqency or weh of a sed by. Here we vew n = as rooronal o. Noe ha we can se eher all he as had aled o he maes n her hoosream, or a sbse of hese as, e.., only hose ha co-occr wh some a n ser's maes. Flckr Case Sdy To show how ser-eneraed meadaa can be sed o ersonale mae search resls, we rereved a varey of daa from Flckr sn her blc API. We colleced maes by erformn a snle keyword a search of all blc maes on Flckr. We secfed ha he rerned maes are ordered by her neresnness vale, wh mos neresn maes frs. We rereved he lnks o he o 4500 maes for each of he search erm. We ndcae he ossble senses of he qery erm below: - er: a b ca e.., Asan er, b shark Ter shark, c flower Ter Lly, d olfn Ter Woods, ec. - newborn: a hman baby, b ken, c y, d dckln, e foal, ec. - beele: a a ye of nsec and b Volkswaen car For each mae n he se, we sed Flckr's API o rereve he name of he ser who osed he mae mae owner, and all he mae's as and ros. We manally evalaed he o 500 maes n each daa se and marked each as relevan f was relaed o he frs sense a of he search erm lsed above, or no relevan, f he evalaor deemed no relevan or cold no ndersand he mae well enoh o jde s relevance. qery relevan no relevan recson newborn er beele Table 1 Nmber of he o 500 mos neresn maes n each search se ha were deemed relevan o he frs sense of he qery erm. The able above reors search recson whn he 500 labeled maes, as jded from he on of vew of he searchn sers. Precson s defned as he rooron of relevan maes whn he o 500 maes. Search recson on hese samle qeres s no very hh de o he resence of false 9

10 osves maes no relevan o he sense of he search erm he ser had n mnd. We do no come search recall, or he rooron of all relevan maes ha are rereved, snce s dffcl for s o esmae how many maes relevan o each search here are on Flckr. Or objecve s o ersonale mae search resls; herefore, o evalae or aroach, we need o have sers o whom he search resls wll be alored. We denfed for sers who are neresed n he frs sense of each search erm. For he newborn se, hose sers were one of he ahors and hree oher conacs whn ha ser s socal nework who are known o be neresed n chld hoorahy. For he oher daa ses, he sers were chosen from amon he hoorahers whose maes were rerned by he a search. We sded each ser's rofle, ncldn ro membersh, ser's saemen, and ser's hoo sream, o confrm ha he ser was neresed n he frs sense of he search erm. For each of he welve sers, we rereved a ls of all as, wh her freqences, ha hese sers have sed o annoae her own maes. The model was raned searaely on each se of 4500 maes, wh he nmber of ocs fxed a en. Comaon of s cenral o he arameer esmaon rocess, and ells s somehn abo how sronly a a conrbes o a oc. Table 1 shows he mos robable 25 as for some of he learned ocs n he er daase. Alhoh he a er domnaes mos ocs, we can dscern dfferen hemes from he oher as ha aear n each oc. Ths, oc 3 s obvosly abo domesc cas, whle oc 8 s abo Ale comer rodcs. Meanwhle, oc 2 s abo flowers and colors flower, lly, yellow, nk, red ; oc 6 s abo laces losaneles, sandeo, lasveas, sard,, resmably becase hey have oos. Toc 7 conans several varaons of er's scenfc name, anhera rs. Ths mehod also aears o denfy relaed erms whch can be sed o exand he qery. Toc 5, for examle, ves synonyms ca, ky, as well as he more eneral erm e and he more secfc erms ken and abby. I even conans he Sansh verson of he word: ao. Reconn amby of as, Flckr searaes maes aed wh some keyword no clsers, wh maes n each clser relaed by meann. For he a er, 3 for examle, fnds for clsers. The frs clser s abo wldlfe n oos, he second abo Ale Comer rodcs, and he hrd abo orane flowers. The forh clser conans maes nved o bes-of ros and aed wh ro names, sch as secanmal, mressedbybeay, ec. Alhoh clsern aears o fnd dfferen senses of ambos as smlar o or oc model aroach, or framework has he added advanae ha he learned ocs or more accraely, he learned robables can be frher sed o ersonale search resls er er er er naonaloo er oo secanmal ca ers er ale anmal anmal le ky dcoo smaraner mac nare abfave ce ercb oo osx anmals flower ken calforna nkon macnosh wld berfly cas lon washnondc screensho jer macro orane ca smhsonan macosx wldlfe yellow eyes cc100 washnon desko lovenare swallowal e florda anmals mac cb lly abby rl ca sevejobs sberaner reen sres wlhelma bca dashboard bljdor canon whskers self rs macbook london nsec whe lasveas anhera owerbook asrala nare ar sar bcas os orfolo nk felne me d70s 104 whe red fr baby anhera...smarae canon derenn flowers anmal aoo dc x orono orane ao endanered smarae od sres easern es llsraon anmal comer amrer sa black?? 2005 book 10

11 nkon...allery mressed aws losaneles anherars nel s5600 a2 frry orra nkond70 keyboard eyes secnare nose sandeo d70 wde sydney black eeh laoo 2006 wallaer ca sreear beafl raffe ov111 lao Table 2 To 25 as ordered by for some of he learned ocs n he er daase. We evalaed model-based ersonalaon by sn he learned arameers and he nformaon abo he neress of he seleced sers o come for he o 500 manally labeled maes n he se. Once maes were ranked by how smlar hey are o ser's neress, we calclaed how many of he o-ranked x maes were relevan o each ser. From hs nmber, we calclaed he recson of search, reored n Fre 5. The hck lne n Fre 5 resens resls of lan search, wh maes ranked by Flckr accordn o how neresn hey are, whle he hn dashed lnes reor recson of ersonaled search resls for each of he sers. As can be seen from he fre, mos of he dashed lnes are above he lan search lne, ndcan mrove relevance for mos sers. The bes resls were for he beele se. Whle fewer han half of he rerned maes were relevan o he nsec sense of he word, ersonalaon flern shed relevan maes hher. In fac, for hree of he for sers, all of he o 100 maes were deemed o be relevan. On he newborn se, ersonalaon enerally heled mrove search resls for all b ser3. For wo of he sers, he o 200 of he flered maes were all relevan. Resls were less mressve for he er se, where lan search oerformed flered search for hree of he for sers. The for chosen sers were all hhly rearded hoorahers, no qe averae Flckr sers, and had wde rann hoorahy neress. The oor erformance of ersonalaon can robably be exlaned by hese sers' breadh of neress. newborn er 11

12 beele Fre 5: Ta-based ersonalaon resls for a search on Flckr for qery words newborn, er, and beele. We cked for dfferen sers for each qery ha were neresed n a snle sense of he qery erm. FUTURE RESEARCH DIRECTIONS User-eneraed meadaa s a rch sorce of nformaon abo ser s ases and references ha can be leveraed o ersonale nformaon o an ndvdal ser. Ths ersonalaon can be aled o browsn and search. In hs chaer we exlored he se of as and ros whch were also vewed as blcly areed-on as for reresenn ser s neress. In addon o as, sers exress her neress n oher ways, e.., hroh he socal neworks hey jon and hroh he conen hey mark as her favore. I s moran o develo alorhmc aroaches ha combne mlle heeroeneos sorces of meadaa o sccncly reresen ser s nformaon references. The ersonalaon mehod descrbed n hs chaer wll fal f a ser makes a qery n a doman n whch she has no revosly exressed any neres. For examle, sose ha a chld orra hooraher wans o fnd beafl monan scenery. If she has never creaed as relan o monans landscae hoorahy n eneral, he ersonalaon mehod descrbed above wll fal. However, he Flckr commny as a whole has eneraed a snfcan amon of daa abo nare and landscae hoorahy and monans n arclar. Analyss of commny-eneraed daa can hel he ser dscover monan maery he commny has denfed as ben ood. We need alorhms o mne commny-eneraed meadaa and knowlede o denfy commny-secfc ocs of neres, vocablary, ahores whn he commnes and commny-veed conen. CONCLUSION In addon o crean conen, sers of Web 2.0 ses enerae lare qanes of meadaa, or daa abo daa, ha descrbe her neress, ases and references. These meadaa, n he form of as and socal neworks, are creaed manly o hel sers orane and manae her own conen. These yes of meadaa can also be sed o are relevan conen o he ser hroh recommendaon or ersonalaon. Ths chaer descrbes a machne learnn-based mehod for ersonaln resls of mae search on Flckr. Or mehod reles on meadaa creaed by sers hroh her everyday acves on Flckr, namely he as hey sed for annoan her maes and he ros o whch hey sbmed hese maes. Ths nformaon cares ser's ases and references n hoorahy and can be sed o ersonale mae search resls o he ndvdal ser. We valdaed or aroach by shown ha can be sed o mrove recson of mae search on Flckr for hree ambos erms: newborn, er, 12

13 and beele. In addon o mrovn search recson, he a-based aroach can also be sed o exand he search by sesn oher relevan keywords e.., anherars, bca and cb for he qery er. REFERENCES Arawal, R., & Srkan, R Fas alorhms for mnn assocaon rles. In Bocca, J. B., Jarke, M.& Zanolo, C. Eds., Proceedns of he 20 h In. Conf. Very Lare Daa Bases, VLDB Moran Kafmann. Breese, J., Heckerman, D.& Kade, C Emrcal analyss of redcve alorhms for collaborave flern. In Proceedns of he 14h Annal Conference on Uncerany n Arfcal Inellence San Francsco, CA: Moran Kafmann. Demser, A. P., Lard, N.M. & Rbn, D.B Maxmm lkelhood from ncomlee daa va he em alorhm. Jornal of he Royal Sascal Socey. Seres B Mehodolocal 391, Golder, S.A. & Hberman, B.A The srcre of collaborave an sysems. Jornal of Informaon Scence 322, Jn, R., S, L., & Zha, C A sdy of mxre models for collaborave flern. Informaon Rereval 93: Lerman, K., Planrasochok, A. & Won, C Personaln Imae Search Resls on Flckr. In Proceedns of AAAI worksho on Inellen Technqes for Informaon Personalaon. Vancover, Canada, AAAI Press. Ma, Z., Pan, G.& L-Shen, O.R Ineres-based ersonaled search. ACM Trans. Inf. Sys Marlow, C., Naaman, M., boyd, d. & Davs, M H06, an aer, axonomy, flckr, academc arcle, oread. Proceedns of Hyerex New York: ACM. Mobasher, B., Cooley, R. & Srvasava, J Aomac ersonalaon based on web sae mnn. Commn. ACM 438, Poescl, A., Unar, L., Pennock, D. & Lawrence, S Probablsc models for nfed collaborave and conen-based recommendaon n sarse-daa envronmens. In 17h Conference on Uncerany n Arfcal Inellence Rosen-Zv, M., Grffhs, T., Seyvers, M. & Smyh, P The ahor-oc model for ahors and docmens. In Proceedns of he 20h conference on Uncerany n arfcal nellence Arlnon, Vrna, Uned Saes: AUAI Press. Schafer, J., Frankowsk, D., Herlocker, J. & Sen, S Collaborave flern recommender sysems. The Adave Web,

14 ADDITIONAL READING SECTION Crane, R. & Sornee, D Vral, qaly, and jnk vdeos on yobe: Searan conen from nose n an nformaon-rch envronmen. Proceedns of AAAI symosm on Socal Informaon Processn SIPS08, Menlo Park, CA, AAAI. K. Lerman Socal nformaon rocessn n socal news areaon. IEEE Inerne Comn: secal sse on Socal Search, 116, Mka, P Onoloes are s: A nfed model of socal neworks and semancs. In Inernaonal Semanc Web Conference ISWC-05. Mslove, A., Gmmad, K.P., and Drschel, P Exlon socal neworks for nerne search. Proceedns of he 5h Worksho on Ho Tocs n Neworks HoNes S06. Noll, M. G. & Menel, C Web Search Personalaon va Socal Bookmarkn and Tan, Proceedns of 6h Inernaonal Semanc Web Conference ISWC, Srner LNCS 4825, Bsan, Soh Korea, Pern, S., Gonçalves, M. & Fox, E.A Recommender sysems research: A connecon-cenrc srvey. Jornal of Inellen Informaon Sysems, 232, Planrasochok, A. & Lerman, K Exlon Socal Annoaon for Aomac Resorce Dscovery, Proceedns of AAAI worksho on Informaon Ineraon IIWeb-07. KEY TERMS & DEFINITIONS Socal Meda: a erm ha defnes acves by whch sers creae and blsh conen on he Web. Examles nclde Flckr, del.co.s, D and many ohers. Socal Web: an mbrella erm ha ncldes socal meda and socal neworkn ses, lke Facebook and MySace. Machne learnn: a sbfeld of arfcal nellence ha s concerned wh alorhms and echnqes for allown comers o learn from daa. Personalaon: alorhms and echnqes ha alor conen o ndvdal sers Imae search: a ye of Web search ha rerns maes machn a ven ex qery Meadaa: daa abo daa Ta: a freely-chosen keyword or erm assocaed wh conen by he ser ENDNOTES 1 Alhoh any ser can a he mae nless secfcally barred from don so by he mae owner, enerally, only he mae owner as hem. 2 Flckr ses a roreary alorhm o evalae how neresn an mae s based on he nmber of mes was vewed, commened on, marked as a favore, amon oher facors. 3 h:// 14