CONTENT RECOMMENDATION SYSTEM BASED ON PRIVATE DYNAMIC USER PROFILE TING CHEN, WEI-LI HAN, HAI-DONG WANG, YI-XUN ZHOU, BIN XU, BIN-YU ZANG Softare Shool, Fdan Unversty, Chna E-MAIL: {05053009*, lhan, 06053008, 0461057, 0361069, byzang}@fdan.ed.n Abstrat: As the amont of the aessble nformaton n the Internet s overhelmng, personalzed ontent reommendaton system offers spam flterng serve and sggests sefl nformaton to the end sers. It s a hotspot n the researh area of ontent management on WWW. Tradtonal reommendaton systems do the data mnng on eb aess logs, dsover ser s aess patterns, and flter the nformaton on behalf of the ser at the server sde. One rtal lmtaton of tradtonal reommendaton system s the lak of ser s prvate daly data, sh as shedles, favorte ebstes and personal emals. The reason for ths lmtaton s the prvay leak sse hen the server holds mh more prvate ser data. To solve ths problem, ths paper presents an agent-based personalzed reommendaton method alled Content REommendaton System based on prvate Dynam User Profle (CRESDUP). The system ollets and mnes the prvate data of ser at the lent sde, dsovers, stores and pdates prvate Dynam User Profle (DUP) at the lent sde. The system fethes preferred message from the ontent server aordng to DUP. An mportant sage of ths tehnology s a personalzed advertsng system n the RSS (Rh Ste Smmary, or RDF Ste Smmary) reader applaton. Or experment shos that the system an tlze DUP to dentfy the stomers potental preferenes and delver the more preferred messages, espeally the advertsements, to people ho are nterested. eyords: Content reommendaton; Dynam ser profle; Prvay-Enhaned personalzaton; Data mnng; RSS 1. Introdton Noadays as the nformaton orld s nreasngly expandng, the problem of the overhelmng nformaton flood s beomng more and more seros [6]. Users are overloaded by thosands of messages and a large qantty of nformaton, most of hh are spam [9]. What they need s a lassfaton tool representng ther preferenes. Content Reommendaton (CR) tehnology [15] an help sers get preferred nformaton aordng to the sers predefned preferenes [14] or some sage patterns mned from eb aess logs [4]. In general, more detaled prvate ser nformaton, nldng personal shedles, emals, reently vsted ebstes, an help CR system to provde more arate ontents. Hoever, ths may ase the prvay leak sse. In tradtonal ontent provdng serves [16], the personal data to mprove the aray of the provded nformaton only nlde eb aess logs, predefned stat preferene lsts, part of the ontat nformaton. Users do not ant to store mh detaled personal nformaton on the server. Besdes, sers mst set ther preferene lsts and ontat nformaton on eah server to get the stomzed serve from dfferent serve provders. In ths paper, e present a novel agent-based personalzed reommendaton system alled Content REommendaton System based on prvate Dynam User Profle (CRESDUP). Ths system s able to fnd ot preferred messages on the Internet aordng to prvate ser data and protet the prvay of ser at the same tme as these personal data are proessed at the lent sde. A prvate Dynam User Profle (DUP) s onstrted throgh data mnng on ser s personal data at the lent sde. DUP s reglarly pdated aordng to the hanges of the personal data drng ser s daly operatons and the ser s feedbaks. Frthermore, DUP an be resed for dfferent ontent serve provders bease t s stored and tlzed at the lent sde. The rest of the paper s organzed as follos. Seton ntrodes the bakgrond and srveys the related ork; Seton 3 presents or personalzed CR method; Seton 4 desrbes an applaton senaro: RSS Ad serve th CRESDUP, dssses some man sses related to or 1-444-0973-X/07/$5.00 007 IEEE 11
method n the senaro and evalates ts performane; Seton 5 dssses some remanng sses; Seton 6 onldes the paper and dssses the ftre ork.. Bakgrond.1. Content Reommendaton To provde more personalzed serve on eb, Web Usage Mnng (WUM) s proposed [16]. WUM typally extrats knoledge by analyzng hstoral data of sers or servers. Web Personalzed and reommender systems [16-17] are typal applatons of WUM. Reommendaton systems emerged as an ndependent researh area n the md-1990s and are sally lassfed nto three ategores, based on ho reommendatons are made []. Content-based reommendatons: The ser ll be reommended tems smlar to the ones the ser preferred n the past; Collaboratve reommendatons: The ser ll be reommended tems that people th smlar tastes and preferenes lked n the past. Hybrd approahes: These methods ombne ontent-based and ollaboratve methods. CRESDUP foses prmarly on ontent-based reommendaton methods sne t need not ommnate th other sers... Content Pshng Serve and Ant-Spam Content Pshng Serve (CPS) an delver sbsrbed messages to ser. RSS s a poplar applaton of CPS. To se RSS, sers sbsrbe to stes and montor the pdates of these stes th an RSS reader. The RSS reader retreves the RSS feed perodally from a server by donloadng the RSS data fle. Hoever, RSS may psh some messages hh are not preferred by sbsrber. The preferenes of sbsrber are dynamally hangng, bt the sbsrbed feeds do not hange so fast to ft hs/her preferenes. If these nnterestng messages are nmeros, the sbsrber may refse to se the CPS. Ths problem does harm to ontent provders, bease these messages old bnd th some advertsements, hh are the man sore of nome for most ontent provders. Ths s a typal serty sse: ant-spam [10-1]..3. Motvaton Ths paper provdes a novel method to reommend preferred message to end ser based on hs/her prvate data. We apply ths method to advertsement reommendaton drng RSS ontent pshng..4. Related Works The ontent-based approah of reommendaton has ts roots n nformaton retreval [1], and nformaton flterng [3] researh. Content-based systems are desgned mostly to reommend text-based tems; the ontent n these systems s sally desrbed th keyords. For example, a ontent-based omponent of the Fab system [], hh reommends eb pages to sers, represents eb page ontent th the 100 most mportant ords. Smlarly, the Syskll & Webert system [8] represents doments th the 18 most nformatve ords. Besdes the tradtonal hersts that are based mostly on nformaton retreval methods, other tehnqes for ontent-based reommendaton have also been sed, sh as Bayesan lassfers [7], and varos mahne learnng tehnqes, nldng lsterng, deson trees, and artfal neral netorks [8]. In the area of ser profle proessng and evolton, Sparano [13] presents a method based on Bayesan netorks to onstrt a profle for provdng stomzed serves to msem vstors. 3. Arhtetre of Cresdp 3.1. Overve To lassfy the messages aordng to the ser s preferenes, e have desgned an ntellgent personalzed ontent reommendaton system. We name ths system CRESDUP (Content REommendaton System based on prvate Dynam User Profle). In fgre 1, the three-ter of the system s demonstrated. Detals ll be dsssed n the follong setons. There are for ore modles n or system: User Ra Informaton Colleton Agent; User Profle Analyss Agent; Content Reommendaton Server; Content Reommendaton Clent Agent. The system onssts of three ters. The frst ter s the data layer, nldng the ser ra nformaton olleton agent. The seond ter s the log layer, nldng the bld-p proess of DUP and the sage of DUP throgh 113
dfferent agents. The thrd ter s the presentaton layer, nldng the stomzed UI (User Interfae) on the PC lents or moble deves. Personal Informaton Reently Vsted URLs My Doments Calendar Favorte Webstes Personalzed User Profle User Ra Informaton Colleton Agent Unfed XML Fle Unfed XML Fle Unfed XML Fle User Profle Analyss Agent Content Reommendaton Clent Agent(on PC, PDA, or Smart Phone) Serve Platform(Broer, PDA,Handy phones...) Data (Extraton) Log(Analyss) Content Reommendaton Server Fgre 1. The CRESDUP system as a three-ter applaton 3.. User Ra Informaton Colleton Agent The ser profle nldes ser s ork pattern and lfe mode. It s reated from the ser ra nformaton data. An agent s employed to do the data olleton ork. It ollets the ra data from the ser s personal data resores loally hh nlde: Bas Personal Informaton: Name, Age, Gender, Loaton, Company, Preferene Lsts, et. Reently Vsted URLs: The ebstes the ser has brosed reently. Self-made doments: The doments hh are desgned, rtten and reveed by the ser, nldng emals, notes and ork doments. Calendar and Shedle: The shedle of a ser, both that of ork and lfe. Favorte Webstes: The ebstes ser adds to the favorte lst of the broser. A feedbak agent s sed to ollet the feedbaks as an mportant sore for the mprovement of DUP. So that DUP an be pdated reglarly and represent the ser s dynamally-hangng preferenes. Presentaton Bease of the dfferent formats of the ra data, they are formatted nto predefned XML fles. 3.3. User Profle Analyss Agent The ser profle analyss agent makes a ser profle from the formatted ra data. In ths system, ontent-based reommendaton algorthm s employed. The ser ll be reommended the data tems smlar to the ones they preferred n the past. In ontent-based reommendaton methods [], tltes(, d), hh s the mportane of a data tem d for ser s estmated from the knon tltes(, d ) assgned by ser to data tems d D hh are smlar to data tem d. After the ra nformaton data are olleted and formatted nto predefned XML fles, the ser profle analyss agent ses the term freqeny/nverse doment freqeny (TF-IDF) [5] measre for spefyng the keyord eghts of the ra data. A ser profle,.e., a set of attrbtes haraterzng tem, s made and sed to determne the approprateness of the nformaton tem for reommendaton prposes. TF-IDF s defned as follos: Assme that N s the total nmber of doments that are olleted and that keyord k appears n n of them. Moreover, assme that s the nmber of tmes keyord k appears n f, doment d. Then TF,, the term freqeny of keyord k n doment d, s defned as f, TF, = max z f z, (1) here the maxmm s ompted over the freqenes f z, of all keyords k d z that appear n the doment. Sne keyords that appear n many doments are not sefl n dstngshng a relevant doment from an rrelevant one. Therefore, the measre of nverse doment freqeny ( IDF ) s often sed n ombnaton th smple term freqeny ( TF ). The nverse doment freqeny for, keyord s sally defned as k N IDF = log () n 114
doment Then, the TF-IDF eght for keyord d s defned as = TF, And the ontent of doment, IDF (3) d s defned as ontent ( d ) = ( 1,..., k ) A doment often ontans dfferent sore types of keyords, sh as sbets, ontents, and other types of attrbtes. Hene, the fnal profle of the doment s made p reated from eghted keyords lsts by the normalzaton of eghted keyords. Fnally, the DUP s a olleton of all the doment profles. 3.4. Content Reommendaton Server The ontent reommendaton server manages the ontents and sends them to the lent aordng to the lent reqest. The ontent reommendaton server organzes the to-be-pshed ontents nto a ategory-tree (Fgre ). Eah non-leaf node of the tree s a ategory hle the leaf node s a pee of ontent. By TF-IDF algorthm, the keyords are extrated from the ra ontents and the ontent profles for eah ategory are mantaned. k n TF-IDF vetors and of keyord eghts, the relaton beteen them an be evalated by some sorng herst, sh as the osne smlarty measre. (, ) = os(, ) = = = 1 = 1,,, = 1, here s the total nmber of keyords n the system. Conseqently, the algorthm ll assgn hgher vales to those ontents that have more smlar featres n the metadata fle as DUP. Then the lent selets these ontents, reates a ontent reqest and sends t to the ontent reommendaton server. The preferred ontents ll be delvered to the lent by the server. 3.6. Man Workflo of CRESDUP User Ra Informaton Colleton Agent User Profle Analyss Agent Content Reommendaton Clent Content Reommendaton Server (4) Hotels Restarants Conerts Moves Arts Collet Ra Data Start Analyss Start Reommendaton Ask for ontent metadata A B C G H I M N O Reply th ontent metadata Send seleted ontents reqest D E F J L Update the DUP Reply th ontents seleted B-1 B- B-3 N-1 N- N-3 Fgre. Category-tree on the server The ontent profles for eah ategory ll be sed as the metadata of the ontents. It s formatted nto XML fles. Wth ths metadata, the server does not have to send the hole ontent to the lent for the seleton and flterng proess. Ths, t saves tme and netork resore. 3.5. Content Reommendaton Clent Agent The lent s an agent to reeve nformaton data from the server based on ser s profle. It an ether be an ndependent modle of the lent-sde softare or an ntegrated part of the lent-sde softare. By reevng the ontent metadata profle from the server, the agent an ompare t th the loal DUP. Sne both ontent metadata profle and DUP are represented as Fgre 3. Workflo of CRESDUP system The man orkflo seqene s llstrated n fgre 3. Frstly, the nformaton olleton agent ollets the data from the ser s daly sages on the ompter. Seondly, th the ser profle analyss agent, a DUP representng ser s preferenes s proded from these data. Thrdly, the ontent reommendaton lent agent ses DUP to selet and flter the nformaton reeved from the ontent reommendaton server. The lent an be deployed on the desktop PC/laptop PC, smart phones, PDAs and other smart lent deves. The hole orkng proess s ondted on the lent sde th no nterferene from pbl servers. 115
4. Applaton Senaro and Performane Evalaton 4.1. Applaton Senaro Desrpton The RSS-based advertsng system presented n ths paper s an applaton of CRESDUP and s sed to demonstrate the featres of the tehnologes mentoned above. Advertsng on RSS readers based on ser s preferenes has large potental de to the very personal and ntmate natre of the RSS applaton. The sbsrbed RSS feeds mply the nterests of the ser. Combnng them th other personal nformaton provdes a prese personal profle of the ser. Offerng valable and related ads hen the ser s readng the RSS data greatly mproves the ad effetveness. The advertsng system s agmented th personalzed profles so that only relevant, targeted ads are pshed to the sers. The system s dvded nto to parts: an RSS reader (Fgre 4) th CRESDUP spport and an advertsement server. The RSS reader s smlar to the tradtonal RSS reader exept for ts advertsng fnton. It ontans an ad bar belo the RSS ve ndo. The ad ontents provded n ths banner are areflly hosen th respet to DUP. Therefore, the ser s able to get the most nterestng advertsements. The ad server manages the ads, lassfes them nto organzed ategores and sends the ads to the ad agent lents aordng to ther reqests. Step 4 Step 1 and Step User Profle Step 6 Step 3 Ad Server Ad Database Fgre 5. Man arhtetre and ork flo seqene of ths RSS advertsng system The man arhtetre and ork flo seqene of ths RSS advertsng system are llstrated n Fgre 5: (1) User starts the RSS reader and sbsrbes some RSS feeds. () The ser ra nformaton olleton agent and the ser profle analyss agent ork together to bld DUP. (3) The RSS reader sends the ad ategory metadata reqest to the ad server and reeves the reply. (4) The lent agent analyses the ategory metadata and omptes the smlarty beteen eah node of the ategory tree and DUP. (5) The ompared reslts ndate the ser s potental nterests of the ads n ths ategory, the hgher the better. Dependng on ths reslt, the ads server sends the orrespondng ads to the RSS reader lent. (6) When the ser reeves and reads the ads, an explt feedbak s made so that the system old also learn ser s preferenes by gvng optons lke I need more and No, thanks. The DUP s dynamally pdated. It s orth notng that the ser s preferenes are kept on the lent sde nstead of the server. Ths the prvay s proteted from beng exposed to the server. Step 5 4.. Performane Evalaton Fgre 4. The Clent UI In order to valdate or applaton, several experments have been performed. The ad server ontans 00 pees of ads dvded nto 10 ategores, nldng moves, ms, fashon, atomoble, toys, sport, shops, obs, health are and PC games. The DUP ra data resores nlde the ser s sbsrbed RSS feeds, favorte ebstes and daly shedles. The eghts of these resores an be hanged and the sm of all the eghts s 1.0. In the 116
experment, the eght of favorte ebstes s modfed from 0 to 1.0 and the other to resores share the remanng eght vale eqally. The frst experment shon n Fgre 6a s the evalaton of the reommendaton qalty. The sers are asked to gve a sore to the ads. The hgher sore ndates the more sefl the ads are. It an be seen that most of the ads seleted by CRESDUP meet the ser s tastes. Fgre 6b s the ost tme of proessng lent reqests on the server. The overhead ntroded s small. Therefore, CRESDUP s effent. transparent and effetve, hh makes the ser s lfe easy. Ths reommendaton system an be extended n several ays, hh nlde mprovng the nderstandng of sers and ontents, norporatng the ontextal nformaton nto the reommendaton proess, spportng mltple ratngs, and provdng more flexble and less ntrsve types of reommendatons. We are plannng to make ths system take addtonal ontextal nformaton, sh as tme, plae, and the ob of the ser, nto onsderaton hen reommendng the related nformaton. Referenes 5. Dssson (a) Qalty of Reommendaton (b) Tme Cost of Reommendaton Fgre 6. The Performane Evalaton Dynam pdatng of DUP s a remanng sse n CRESDUP. We se a smple offlne solton to pdate DUP. A ne DUP s perodally reated. If the ost tme of reatng DUP s rtal, the onlne ay to pdate DUP s a better opton. It an rede the pdatng ost of DUP. 6. Conlson and Ftre Work Based on personalzed ontent reommendaton serve, the nformaton lassfaton and flterng are done atomatally. The ntellgent nformaton proess s [1] Baeza-Yates, R., Rbero-Beto, B.: Modern Informaton Retreval. Addson-Wesley. (1999) [] Balabanov, M., Shoham, Y.: Fab: Content-Based, Collaboratve Reommendaton. Comm. ACM, vol. 40, no. 3, (1997) 66-7. [3] Belkn, N., Croft, B.: Informaton Flterng and Informaton Retreval. Comm. ACM, vol. 35, no. 1, (199) 9-37 [4] Ron,.: Mnng e-ommere data: the good, the bad, and the gly. Proeedngs of the seventh ACM SIGDD nternatonal onferene(001) [5] Jng, L., Hang, H., Sh, H.: Improved Featre Seleton Approah TFIDF n Text Mnng, Pro. 1 st Internet Conferene on Mahne Learnng and Cybernets, Beng (00) [6] Al, F.F. and Don, H.D.: Manageral nformaton overload. Comm. ACM, vol. 45, no. 10, (00) 17-131 [7] Mooney, R.J., Bennett, P.N., Roy, L.: Book Reommendng Usng Text Categorzaton th Extrated Informaton. Pro. Reommender Systems Papers from 1998 Workshop, Tehnal Report WS-98-08. (1998) [8] Pazzan, M., Bllss, D.: Learnng and Revsng User Profles: The Identfaton of Interestng Web Stes. Mahne Learnng, vol.7, (1997) 313-331 [9] Thede, L.,Marshall, V.A.,Rk W.:An Eonom Anser to Unsolted Commnaton. EC 04. (004) [10] Goodman, J. and Ronthate, R.: Stoppng otgong spam. In Proeedngs of the ACM Conferene on Eletron Commere (EC 04) (Ne York, May 17-0), ACM Press, Ne York, 004, 30-39. [11] Saham, M., Dmas, S., Hekerman, D., and Horvtz, E.: A Bayesan approah to Flterng Jnk e-mal. In Learnng for Text Categorzaton Papers form the AAAI Workshop. AAAI Tehnal Report WS-98-05 (Madson, WI, 1998). [1] Goodman, J., Cormak, G. V., Heherman, D.: Spam and the Ongong Battle for the Inbox, Comm. ACM, Vol 50, (Feb., 007): 5-33. [13] Sparano, F.: Sto(ry)hasts: A Bayesan Netork 117
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