Visual Targeted Advertisement System Based on User Profiling and Content Consumption for Mobile Broadcasting Television

Transcription

1 Viual Targeted Advertiement Sytem Baed on Uer Profiling and ontent onumption for Mobile Broadating Televiion Silvia Uribe Federio Alvarez Joé Manuel Menéndez Guillermo inero Abtrat ontent peronaliation i one of the main aim of the mobile media delivery buine model, a a new way to improve the uer' experiene. In broadating network, the ontent i ent "one to many", o a omplete peronaliation where the uer may elet any ontent i not poible. But uing the mobile bidiretional return hannel (e.g. UMTS onnetion) viual targeted advertiing an be performed in a imple way: by off-line toring the advertiement for eletively replaing the normal broadated advertiement. In fat, thee onept provide powerful method to inreae the value of the ervie, mainly in mobile environment. In thi artile we preent a novel intelligent ontent peronaliation ytem for targeted advertiing over mobile broadating network and terminal, baed on uer profiling and lutering, a a new olution where the ue of ontent peronaliation repreent the ompetitive advantage over traditional advertiing. Keyword ontent peronaliation targeted advertiing mobile televiion lutering profile egmentation DVB-H 1 Introdution Mobile broadating network, uh a DVB-H, have opened new poibilitie and new hallenge to the ator involved in the value hain. But thi evolution hould be followed by the proviion of innovative ervie whih an help uer to atify their need and to fulfil their expetation. In broadating network, the ontent i ent "one to many" o a omplete peronaliation where the uer may elet any ontent he/he want i not poible. For mobile ontent delivery, 3G/4G network an be ued, but on one hand, ontent provider prefer to deliver ontent to mobile via thee broadating network to take advantage of the Digital Terretrial Network [ ], and on the other hand they are otly for ditributing the ame ontent with enough quality to large number of terminal, for example, in Uve event. But with the help of the return hannel, a hort piee of ontent an be off-line reorded and played when appropriate, in thi ae, a a ubtitution of the regular broadated advertiement, thu giving the uer the advertiement whih i of hi/her interet, and the advertier a more targeted way to deliver advertiement, inreaing it effetivene. In thi paper we preent a novel intelligent ontent peronaliation ytem for targeted advertiing over mobile broadating network, baed on uer profiling and lutering, and taking advantage of the onumption data obtained from the uer a well a the information given by uer' tate and behaviour. If we apply ontent peronaliation on advertiing environment, both utomer and ontent provider an benefit, beaue on one ide utomer reeive only the publiity they may be intereted in, and on the other ide advertier an reah their potential publi with higher effiieny.

2 The main differene between thi ytem and other reommendation appliation i that our ytem provide a tranparent olution for uer, by allowing them to reeive the ontent aording to their profile and behaviour when onuming ontent, without needing their interation. Thi i done by linking the advertiement and the uer or uer, baed not only on data extrated about their oio-eonomi profile, but alo in the real behaviour when interating, their real media onumption obtained uing audiene meaurement tehnologie, and their oial link. For a ommerial ervie deployment, it i poible to ak the uer to regiter to the targeted advertiement ervie, reeiving premium ontent and eonomial advantage beaue of the ubription to the ervie. The ytem that we deribe in thi paper an be applied to broadband mobile delivery network, uh a 3G/4G, where the ontent i delivered uing the mobile hannel, and ued for both broadband and broadat network. We think that for mobile broadating network, our propoal may have wider impat. The ret of the paper i organied a follow. Setion 2 outline the tate of the art of reommendation and peronaliation ytem, foued mainly on mobile terminal. In Setion 3, the deigned ytem i deribed in detail. In Setion 4 the laifiation, lutering and targeting i explained. The ytem operation i widely explained in Setion 5. And in Setion 6 the ytem tet and evaluation method ued for validating the implementation i explained. Finally, onluion are preented in Setion 7. 2 Overview of the prior art in peronaliation ytem in the media environment Although there are many olution that an be hoen, depending on the available mobile televiion tehnology, the mot immediate way of delivering mobile broadated TV i via re-broadating the regular TV hannel. Thi trategy ha been followed by many broadater in the world, and provide the ame ontent but in different devie, whih lower the uer' experiene due to it diadvantage, uh a a maller diplay ize or a lower battery and proeing power. Thi ommon TV broadating olution doe not harne the potentialitie of mobile environment. onequently, it i needed to find new funtionalitie to attrat uer and advertier to thi mobile broadating ontent environment. Multimedia ontent peronaliation repreent a wide field of tudy and reearh. It main objetive are to obtain an intelligent ontent management, and to ontribute to improve the uer' experiene. One of the mot ommon way to improve thi experiene i by developing ontent reommendation ytem baed on uer' preferene and behaviour. The mot important tehnique ued for thi kind of proee are lutering and profile uer' laifiation, whih are widely applied in different environment, uh a web browing [2], or even in televiion appliation [3], With the advent of reommendation ytem (whoe main methodologie are widely explained in [4]), and their evolution in peronaliation ytem, it i expeted that uer and their need and hoie beome the entre of the multimedia ervie' deign. Reently, the number of reearh work ha inreaed in thi field. In fat, peronaliation ytem are applied in everal environment with different objetive, a it i hown in [5], where a omplete ytem of peronalied reommendation for ubiquitou hopping (baed on ollaborative filtering and a ontent baed tehnique for defining utomer' profile) i deribed. Some work preent ytem whoe objetive i not only to provide peronal ontent, but to reate peronal ontent, that i, to reate ummarie whih ontain fragment of relevant ontent for the uer. In [6], the author preent a ytem baed on uer' profile, and on the metadata obtained from the emanti annotation of video event, uing webating textual data. And in [7], the author explain the different method to reate thee ummarie aording to uer profile and multimedia feature. ontent peronaliation repreent an important advane in mobility, even though with limited apabilitie in the terminal ompared to fixed reeiver, that i, limited proeing apabilitie and battery power, mall memory apaity, et. On the other hand, uer pereive mobile devie in an inherently peronal way, whih repreent an important fator to be taken into aount. In thi line, peronaliation line are baially two: Struture peronaliation, whih foue on the ontent onfiguration itelf: it ize, it format, the ue of exiting funtionalitie, et. An example i given in [8], where the main objetive i to allow uer to ae the ame ontent in different devie with different apabilitie, uh a P, mobile devie... ontent peronaliation, whih foue on the ontent itelf, baed on fator uh a uer preferene. The propoed ytem foue on thi kind of peronaliation. Peronaliation ytem are alo applied to the televiion environment, due to the new apabilitie in digital televiion. Different ytem tarted ome year ago. For example, in 2001, one of the firt peronaliation ytem, alled imedia [9, 10], wa preented, repreenting an important advane in peronaliation reearh. Later, other

3 method and appliation an be found in the literature, a the one explained in [11], whih deribe a peronalied TV program reommendation ytem, baed on ontent filtering tehnique and ollaborative filtering to eae the finding of the right ontent for eah uer, aording to their preferene. Although the ytem preent intereting advane, it i not olving the targeting of the advertiement to the uer taking into aount the uer ontent onumption meaurement and uer tate. In peronalied ytem for advertiing, we may found relevant advane too. In [12] the author ugget a omplete ytem for peronalied advertiing on digital televiion but not foued on mobile broadated televiion a the olution explained in thi paper. In addition, ome ommerial ytem and proedure an be found in different patent. For example, in [13] the author propoe a method for automatially adding an advertiement to the beginning or the end of a media file, uh a a podat epiode, when the media file i requeted by a onumer. Thi i imilar to our approah but not running inerting peronalied advertiement in real time in the media tream. In [14] method and arhiteture are diloed for performing direted marketing in lient appliation, uing loal tatiti. Our ytem provide a global ytem whih i propoing tehnique for grouping, lutering, laifiation and targeting, ueful for mobile TV environment in real time. 3 ontent peronaliation ytem for targeted advertiement in mobile ontent delivery network The deign of our ytem omprie different ation in eah of the individual element of the broadating hain. It i not poible to implement a funtional ytem not only without uer' involvement (e.g. information over the return hannel), but alo without ontent provider and broadater. For thi reaon, in thi etion we are going to explain the module that form the ytem and their funtionalitie. Beide, we deribe the operation algorithm deigned to enable the peronaliation proe. 3.1 Sytem arhiteture A it i hown in Fig. 1, the general ytem arhiteture i ompoed of different part. In order to implify the explanation, we have laified them aording to their funtionalitie: 1. The general ontent provider: thi ator i in harge of providing the ontent to the uer, aording to the hoen tehnology. It an be a broadater if any broadating network i available (uh a DVB-H), or a multimedia treaming provider over a ommuniation network, uh a UMTS. Fig. 1 Sytem arhiteture Broadating network: DVB-H Mobile devie Streaming Return hannel Appliation erver/ontent erver/ interativity erver ontent Broadater Uer A UerB Uer

4 2. Final uer: they are repreented by their devie and appliation, whih are in harge of diplaying the ontent (the generi and the peronalied) and allowing the bi-diretional ommuniation through the return hannel (return of uer' information about tate and behaviour when onuming ontent from the erver). 3. ontent peronaliation erver: it i in harge of mathing the right ontent with the right uer or et of uer, aording to the eleted parameter. Thi proe i poible thank to the information reeived from the uer over the return hannel, and to the ontent laifiation and target eletion. 4. ommuniation network: it make poible both ontent proviion in a multimedia ontent treaming environment, and data exhange between uer and ontent peronaliation erver. Although thi ytem i deigned to be ued on both kind of delivery network, that i, broadat televiion over DVB-H or treaming televiion over mobile network, it i important to ay that our olution fit better in mobile broadating network, where imultaneouly a great number of uer may be aeed, a it i explained in [15]. Figure 1 depit the overview of the ytem. While the broadater provide a generi multimedia tream that an be reeived by the uer (part 5 in the figure), the ytem i able to replae the regular advertiement with other loer to uer' tate and onumption behaviour. Beide, when the targeted advertiement i finihed, the appliation return to the regular broadated hannel, thu produing a eamle operation in front of the final uer. A poible problem ome when the targeted advertiement piee i longer or horter than the regular. In thi ae, the ytem i prepared to ommuniate not only the intant when the ontent i replaed but alo the duration, thu foring the advertiement to be eleted with a imilar duration or to overlap with the regular broadating. 3.2 Sytem module and data flow The implementation of thi ontent peronaliation ytem foue on the development of part 2 and 3 of Fig. 1 (Fig. 1 number 2 and 3). In fat, in order to obtain a modular arhiteture, both the erver ide and the uer ide are divided into different element, aording to their funtionalitie. Thee module and the data flow between them are hown in Fig Uer ide A omplete and funtional appliation i intalled in the uer terminal, downloaded via the return hannel (that i, in the uer devie) to enable the peronaliation proe. It main objetive are both the preentation of the peronalied ontent, and the implementation of a return hannel whih provide eential information to the erver for the peronaliation, uing the ontent onumption apture module (to be deribed in Setion 3.2.3). A it i hown in Fig. 2, there are five main module aording to their funtional aim: - ontrol module: it i the ore of the implementation in the uer ide, ine it manage the appliation operation and the data flow. It firt funtion i to preent the different interfae of the appliation: the login interfae (to omplete the regitration proe), and the uer profile interfae, ompoed by different form that uer have to omplete to reate their profile. The anwer to the form help minimiing the o alled 'old tart' effet, and they are ued to perform the firt uer egmentation. Seondly, thi module i in harge of alling the player module to preent generi ontent a well a aking the data bae for the right peronalied ontent to be diplayed. Finally, it i reponible for enquiring the erver for the time left to the next advertiement hange and for apturing uer' inter- Fig. 2 Sytem module ommuniation and USER SIDE SERVER SIDE AI DB ommuniation ommuniation 3n! ontrol Metering Player \W U Data flow inide eah l module of the ytem Data flow between

5 ation, whih repreent eential information to update uer' profile aording to their behaviour. - Player: it i reponible for preenting both the broadater/treaming multimedia generi ontent a well a the peronalied one from the peronaliation erver, when a pot i indiated by the ontrol module. - Ma torage module (DB): thi module tore the peronalied ontent from the erver until it i diplayed by the uer player. Beide, it tore the uer' ontent onumption behaviour data needed to update uer' profile. - ommuniation module: thi i the module in harge of implementing the ommuniation between the uer and the erver ide. By opening a eure oket, it enable the uer information delivery (login data, uer behaviour information, et.), and to reeive peronal ontent from the erver ide. A protool meage i reated to model the information flow (Table 1) in order to et the ommuniation. - Metering module, whih i in harge of apturing ontent onumption, a explained in Setion Server ide The peronaliation erver ide i ompoed of a erver with two main funtionalitie: uer' lutering and egmentation (by reating and updating uer' profile), and providing targeted ontent to the uer aording to thi egmentation. Like the uer ide, the erver ide i alo divided into the following module (ee Fig. 2): - ontrol module: a the one in the uer ide, thi module i in harge of managing the erver operation. It main objetive i to format the information and to apitalie the data flow between module. - Artifiial Intelligene module: it ha the ontrol of uer' profile and the apability of updating them aording to the information obtained. Baed on the uer' lutering and egmentation, thi module i in harge of aoiating eah uer to the tailored advertiement. Ma torage module (DB): it tore the uer' information reeived over the ommuniation module, the profile and the advertiement uploaded by the advertier through the web erver. - Web erver: it hot the web ervie to manage the ytem and ontent. Broadater and ontent provider an reate, modify and delete uer' aount. Advertiing agenie an reate new ommerial ampaign, upload new ontent and peify the target group. In addition, it ha a module to tudy and preent uer' onumption tatiti ontent onumption apture module In order to apture the ontent onumption by the uer, a ontent onumption apture module hould be implemented in the uer terminal. In thi ae, we followed the guideline from [16], where the audiene meaurement for mobile broadating i explained. In our ae, the ytem gather the information uing the following module (Fig. 3): Broadater (media aet and interative ervie provider): whih deliver ontent and the aoiated metadata to the uer. Metering: it i in harge of performing the meaurement of the onumed ontent. The Metering an be developed Table 1 Meage flow and ommuniation protool between erver and lient ide Meage flow Meage truture Funtion Anwer Uer->Server 'luer@pa-l' onnet to the erver 1= onnet Uer: uer name Pa: uer paword 0= authen. error Uer->Server '6han-l' han: hannel Indiate whih annel the uer i wathing Uer->Server '5-1 Ak for the eond to next hange Se left for the hange Uer>Server 3 e/when-1' Se: peronal ontent time length. When: hange time Ak for the new ontent to tore it in the devie The ontent itelf, aording to the length indiated on the 'e' field Uer>Server 4uer@adv#een-l' Adv: ontent id. ode Send info about uer behaviour Seen=Boolean: 1= ontent een. 0= ontent not een. Uer>Sever '2uer@pa-l' Dionnet

6 Fig. 3 ontent onumption apture ommuniation SERVER SIDE USER SIDE either a a eparate devie or a a oftware omponent. In the ae of mobile devie, it i a reident oftware whih i ativated when the mobile terminal or PDA open the programme to wath mobile TV. Data Bae and Artifiial Intelligene Module: in harge of olleting and proeing the data provided by the Metering module and the ontent provider. Additional module for performing the meaurement, a the following: The loal or tored Audio-Viual (AV) oure: not only the information preented may ome from the regular TV delivery, but alo from a loal AV oure, uh a a tored video played through a media player. In thi ae, a metadata may not be preent, we may not be able to apture additional information on the ontent. The module for preenting the information or deoding the AV ignal (inide the mobile terminal): thee are regular element of a broadating hain with whih the Metering module hould interat. A the meaurement operation i not the main fou of the preented ytem, we jut explained the general module whih perform the meaurement operation. Wider information an be found in [16], 4 laifiation, lutering and uer targeting To ahieve our objetive of targeting the advertiement to the uer, trying to meet the uer tate to enhane the advertiement impat by peronaliing it, we need to elaborate a profiling and lutering method to deliver the targeted advertiement in real-time to the eleted uer or uer group. Thu, the firt tep i to define a model whih an link the advertiement and the uer or uer, baed not only in data extrated about their oio-eonomi profile, but in the real behaviour when interating, their real media onumption obtained uing audiene meaurement tehnologie and their oial link. The normal proedure i to identify and laify the uer into different group, regarding different onidered parameter. Method a [17] infer identity from uer behaviour uing Bayeian tatiti appliable to TV program onumption. Other appliation, not for TV but for Internet onumption modelling [18], deribe ome method for Internet uer behaviour analyi baed on ae trae and it appliation to diover ommunitie baed on a elf-imilarity model. In the oming etion, we will explain the uer profiling method, the uer lutering and the targeting proedure. The propoed proedure and algorithm are baed on audiene meaurement tehnique to apture the information about the ontent onumption: whih hannel the uer i wathing, if he/he ha eleted an eletroni ervie guide, if uing ubtitle, whih audio mode and language ha been eleted, et. The objetive i to determine whih advertiement may fit to a group of uer, a we aume that we annot reate an advertiement per eah uer, therefore hould be peronalied to a group. Thi group of uer i formed by lutering them aording to their profile imilarity. Then, the et of advertiement whih we have in our databae it i targeted to the group, aording to the proee defined. We are going to define an example along Setion 4 to eae the reader to undertand the proe. 4.1 Uer profiling The approah we have followed in our ytem i to perform the laifiation, lutering and uer targeting by uing the behaviour of the uer, updated regularly to have a real time ytem (with the ontent and the profiling of the uer aording to the media onumption, the tate and the imilaritie to other uer) intead of only attending to the given information on the uer (oio-eonomi level,

7 ontent ranking, total number of hour of ontent onumption, et.). The firt tep, then, i to define a uer profile whih an be modelled to laify the uer. For thi purpoe, we define a et of uer U={Ui,U 2,,U }, holding a peifi profile Up whih we tore in our erver. The onumption of eah uer i obtained aording to the tehnique peified in Setion 3 of thi paper, by uing audiene meaurement tehnique whih apture every eond or minute (depending on the ytem deigner) the information regarding the ontent onumption of the uer and the additional information needed from the broadater and the additional information uh a metadata, audio mode, et. Then, it i tohatially modelled a m (U ), whih i the onumption of a uer regarding different ontent ategorie peified, whih we will all A g. In our ytem, we follow onventional ontent ategorie peified in the TV Anytime peifiation orrigenda, a defined in [19]. So, an example of the uer onumption tohatially modelled i the tatiti of the onumption time of TV programme, laified in the different ategorie over a long time period. For example, in broadated mobile TV, the ame ontent ategorie are repeated periodially (e.g. every Saturday night football mathe are broadated), allowing u to reate the tatiti uing the long time interval and the eleted repetition interval. Therefore, in our example we reate a n- dimenional random variable where eah "dimenion" i the onumption per ontent ategory (uh a port, fition, et.) meaured in minute per week. Thi random variable may be harateried in our example by a Gauian variable with mean 120 min and variane 50 for the ategory "port", mean 160 and variane 40 for the ategory "fition"... But the uer profile annot only be elaborated with the uer onumption but alo with the harateriation of the uer tate. Therefore, we identify the Uer Profile regarding the uer ontent onumption and the Uer Profile regarding the inferred uer tate. We name the n-dimenional tohati proe Uer Profile regarding the uer ontent onumption a U p. Thi U p an be modelled a the onumption of a eleted ontent ategory or ontent ategorie A g (depending on the laifiation of ategorie for the advertiement) whih are the dimenion of the tohati proe during a defined time interval (T*N T ), inluding the repetition time eleted or period (I), where N T i the number of time the period i repeated. N ZMiUiliT^Ag)) To tart the laifiation, we need to define the ontent ategorie whih are linked to the advertiement ategorie, and then we model the different random variable of thi tohati proe, a for example, the Uer Profile onerning the peifi advertiement ategorie. In our example, U p ontain the random vetor of the onumption, partiularied by the ontent ategorie, the repetition and total time interval and, in addition, normalied by thi repetition and total time interval to avoid ditortion due to a different meauring interval. If we want to ahieve ingle figure from U p, we an ompute it by uing the um of eah of the ontent ategorie A g, identified uing what we defined a MGT, the ontent Meaured Granularity Threhold, whih are the number of ontent ategorie ued to model the ytem (whih i ued to implify or adapt the ytem to the number of advertiement to be delivered to the uer). UPG = MGT J2 U P ( 2 ) One we have formulated the general Uer Profile regarding the ontent onumption, we hould etablih the Uer Profile onidering, in addition, the ubjetive ontent appreiation from the uer or panellit. In thi ae, ome onideration hould be made. Firtly, the ubjetivity whih i inerted in the alulation hould be arefully proeed, and the orreponding weighting applied. Seondly, the ubjetive meaure hould be modelled with the orreponding inertitude of the ubjetivity inerted in the Uer Profile regarding the ontent appreiation of the uer. We name the n-dimenional tohati proe Uer Profile, regarding the ubjetive ontent appreiation of the uer, a U p. The ubjetive appreiation of the ontent by eah uer an be modelled eparately or taking into aount the oial environment of the uer regarding the ontent onumption and ontent appreiation. MGT N Where L(U) i a meaure of the appreiation of the ontent per uer. Thi meaure an be obtained uing different approahe. The eaiet way i to obtain a rating of the ontent appreiation by mean of aking the panellit to fill in urvey. Thi i not the bet method, but ueful a a good tarting point. Other method inlude the development of more omplex quetionnaire and tet to know, with better preiion, the ubjetive ontent appreiation. That i why we hould leave the method to obtain L(U) open and, on the other hand, give our etimation to be alulated. In thi ae, we ued the information given in the ubription to tart elaborating a general L(U).

8 Uing the ame example a for the Eq. 1, the Eq. 3 an be onidered a a vetor or alar of the interet of a uer in eah ontent ategory in the ale from 0 to 1, where 0 mean no interet and 1 the maximum interet. The final uer profile alulation i the following: U P =UPG-(1+K)-U J PS (4) In our example, the final profile i ompoed by the tatiti of the onumption and the tate, weighted by a fator K, whih give more or le weight to one part of the uer profile or to the other (depending on the ytem deigner deire). 4.2 Uer lutering One the uer profile ha been etablihed, we need to luter the uer in different group to allow the ytem eleting the target advertiement, a it i not poible to deign one piee of ommerial ontent per uer (beaue of monetary reaon), o we need to luter the uer who will reeive the advertiement. In our ae, the lutering tehnique to be ued hould meet the following requirement: annot be a fixed lutering olution a the ytem, a explained in the introdution, may require to be upervied by the media ageny or by the operator of the ervie. The uer may be part of one or more luter, o we need a kind of "luter belongingne". The lutering algorithm hould onverge in a reaonable time. It ha to be onidered that it may require a large number of operation due to number of uer whih an ubribe to the ervie. The luter hould not be fixed, and the entroid of the luter may hange to be adapted to the ytem operator need or to the uer onumption evolution. The lutering algorithm that ha been eleted i the fuzzy -mean method. There are everal kind of lutering algorithm: Hierarhial lutering Algorithm, Partitional Algorithm, Mixture-Reolving and Mode-Seeking Algorithm, Nearet Neighbour lutering, Fuzzy lutering, Artifiial Neural Network for lutering, et. [20, 21], A deribed in [20], non-fuzzy lutering approahe generate partition; in a partition, eah pattern belong to one and only one luter. Hene, the luter in a hard lutering are dijoint. Fuzzy lutering extend thi notion to aoiate eah pattern with every luter uing a memberhip funtion. The output of uh algorithm i a lutering, but not a partition. In ummary, we elet the fuzzy algorithm beaue i an intereting onept whih provide additional information to a data analyt (in thi ae e.g. the media ageny) due to the proviion of memberhip value and not a hard lutering, and within the fuzzy algorithm, it provide reaonable omputational requirement. To um up, it ha a advantage, a) to provide additional information to a data analyt (in thi ae e.g. the media ageny) due to the proviion of memberhip value and not a hard lutering, b) the reaonable omputation time, and ) the openne to the management by a upervior (to etablih threhold value for the belongingne to a luter). The fuzzy -mean algorithm i ued to obtain the entroid and the memberhip value of the different uer, aigning eah one to a luter. The lutering i omputed through the minimiation of the following objetive funtion: <Jm. TV =1 7=1 Where: 1 < m < o (5) m>l, medi; u j i the degree of memberhip of x, in the lutery; x, the i-th term of the n-dimenional input data;, the n-dimenion entre of the luter omplete profile form Send form to the erver Finih peronalied ontent Preent peronalied ontent Fig. 4 Uer operation algorithm Ye lient log in Start TV Send hannel info Ak for next ommerial break time/reeive next peronalied ontent End Send onumption info

9 onnetion requet and i any norm meauring the imilarity between the input data and the entre. End The proe i iterative, and the optimiation of the objetive funtion / i done by updating the memberhip illy) and the luter entre (j) a follow: Reeive form E t=i 1 (6) Firt uer lutering Selet and end peronalied ontent Send next hange time Reeive onumption data Update uer profile N i=\ N E< The iteration top when maxy {\iii/ k+^ w/,(a;) } < e where e i a termination riteria between 0 and 1, with iteration tep k. Following our example, the uer profile i ued a the input of our lutering algorithm, and the entroid of the luter repreent the "mot ommon" or the "tandard profile" of uer with imilar "uer profile". Then, we an elet the number of luter by different mean: uing the minimum ditane between luter, a maximum number of luter, baed on the ytem deigner deiion on how the luter hould be, et. (7) 4.3 Uer targeting Fig. 5 Server operation algorithm fn our ae, we elet the uer in a luter with imilar Uer Profile, and then deliver the mot adequate targeted advertiement. The aim of thi algorithm i to laify the uer in different luter whih are not pre-etablihed. Thi algorithm Fig. 6 Sytem module interation Uer behavior data USER MOD. Uer behavior data Peronal ontent/timetamp Peronal ontent/timetamp Deior Uer behavior data & laifiation Peronal ontent/timetamp Uer behavior data SERVER MOD. Uer intent/data laifiation I Data Bae * : Web Server T Statiti ontent Uer regitration data Uer behavior data

10 Fig. 7 Sreen hot of the uer appliation operation i defined to luter the different uer in group or ommunitie. The number of group or ommunitie an be etablihed by the ytem deigner aording to a maximum number or aording to a maximum ditane to the luter entre. In order to luter the uer, we ue the parameter m (U\ A^-J to etablih the ommunitie. The lutering an be done by mean of everal algorithm and ditane kind, offering better or fater reult. In our ae, we have eleted the Fuzzy -Mean lutering (FM) algorithm to form the luter, a explained in the former etion. In our ae, we have reated the algorithm defining beforehand the number of luter, whih may be hanged whenever it i required by the ytem deigner. It i poible in addition to let the algorithm to deide the number of luter, but due to ommerial reaon, it wa preferred to elet them in advane. The algorithm work a follow: 1. Define the maximum MGT 2. Inlude the defined ontent ategorie. 3. If the A g are different between member of the ame group, proeed to retrit the alulation to the ommon 4. alulate the m (U ) from eah U. 5. Set the number of target luter and initialie the algorithm, aigning weight to the oeffiient (a we know whih luter we want to reate, we an fine-tune our initial ondition in eah T of the n-dimenional tohati proe). 6. Form a uer onumption pattern n-dimenional random vetor, where eah dimenion i A g (from the tep Table 2 Mapping of the publihed onumption data per ontent ategory ontent ategory onumption in % Non-fition Sport Leiure/hobby Fition Amuement Mui and dane Interative game Other 18,49 5,49 1,65 36,64 35,93 0,68 N/A 1,12

11 Table 3 Stratifiation of the onumption for the imulation Age group to to to 24 4 to 12 % of the total onumption 24,2 28,4 30,4 10,8 6,2 Minute of onumption per day 310 3), aling the umulated onumption in a pre-defined time period. 7. Uing the vetor of the tep 6 regarding the mean of the random vetor, then 259 a. Initialie U(0). b. alulate j with U(k), aigning to the entre the expeted value of the entre of our luter in the form of a n-dimenional vetor. If it i far from our expeted reult, repeat 7.a. with a new U(0).. Update U(k- >k+l). d. If U(k+ 1)-U(k)<e, then top, otherwie repeat. 8. Verify the memberhip reult and aign member to luter aordingly. In our example, the alulation an be implified uing the fitting etimation of Gauian random ditribution whih, in pratie, are the ditribution whih we enountered in the tudied proee. 5 ontent peronaliation ytem operation Due to the ytem arhiteture explained before, there are many module involved in the peronaliation proe. For thi reaon, and in order to detail the ytem operation in a more undertandable way, the explanation of the ontent peronaliation ytem i going to be divided into two part: the final uer appliation operation, and all the proee done in the erver ide. 5.1 Uer appliation operation Figure 4 how the operational diagram of the final uer' appliation. A it i hown, it an be divided into everal tep: 1. lient log in the ytem by ending their onnetion data (uer&paword) ompleted via the interfae appliation diplayed on their devie. 2. If the onnetion data i orret, and if it i their firt time uing the ervie, uer are alo aked to omplete a imple form to extrat their bai profile and preferene (to minimie the o alled 'old tart' effet). After thi tep (or if it i not their firt time uing the ervie), they are onneted to the ytem. 3. Uer tart wathing TV. In order to allow the erver to know the TV hannel the uer i wathing, every time a uer hange the hannel the erver i informed via the ontent onumption apture module. 4. One onneted, the lient module ak the erver for the exat time of the next advertiement break and for the next peronalied ontent to be diplayed (aording to the available duration of the break). Thi ontent i tored in the devie before it i hown. 5. While the uer i wathing hi TV, the ontrol module of the ontent onumption apture module monitor all the ation performed by uer, uh a tuning the hannel, withing off, ae programme guide, et. Thee data are tored in the ma torage module on the lient ide, and they are ent to the erver a a XML file via the return hannel. 6. When an advertiement break ome, aording to the "hange time" reeived, the lient ide wap between regular ontent and the peronalied one, previouly tored on the devie. Thi wap i made without needing any ation from the uer, and avoiding diturbing the Table 4 Ditribution of the onumption per ontent ategory per age group Age group to to to 24 4 to 12 ontribution in minute of the total onumption 75,02 73,556 59,28 15,552 9,362 Perentage of total minute of onumption Non-fition 18,49% Sport 5,49% leiure/hobby 1,65% Fition 36,64% 32,23% 17,18% 3,78% 2,16% 35,34% 31,60% 17,26% 7,19% 1,69% 36,13% 25,47% 21,97% 5,79% 1,39% 35,03% 6,68% 22,55% 6,49% 0,93% 38,11% 4,02% 9,87% 2,3% 0,2% 58,82% Amuement Mui and dane Interative game 35,93% 0,67% N/A 40,11% 0,31% N/A 36,21% 0,4% N/A 33,58% 1,12% N/A 28,59% 2,21% N/A 27,41% 0,28% N/A Other 1,12% 1,12% 1,12% 1,12% 1,12% 1,12%

12 regular uer' ontent viewing. Then, upon finihing, the ytem return to the ommon media flow. 7. When viewer top wathing TV, the lient module end again the onnetion data to the erver along with the onumption data to log out. After thi tep, uer are dionneted from the ytem. O ^H w* "3, S3 tó B O ^H -o Pá 3 5- a-. 5- (Ñ 5.2 Server operation Figure 5 how an operating diagram of the peronaliation erver. A the previou one, it an be alo divided into everal tep. 1. When uer log into the ytem by ending their onnetion data, the erver ide validate it information. 2. If thi information i valid, and if it i the firt time that the uer i aeing the ervie, the erver reeive the peronaliation form ompleted on the uer ide in order to allow the firt uer' laifiation and lutering. 3. After thi laifiation (or if it i not the firt uer' time on the ervie), the AI module on the erver elet the optimal ontent for eah uer or group of uer, aording to the algorithm propoed in Setion 4 of thi paper, and end it bak to them. 4. Later, erver end the next break time to eah uer, aording to the hannel they are wathing. 5. Thi operation equene repeat until the uer ak for being dionneted. If it i o, erver firt reeive the onumption data in order to update uer' profile and lutering, and then dionnet them. To um up, Fig. 6 how the interation between module and the ommuniation flow. 6 Sytem tet and evaluation proedure The ytem wa implemented and teted to validate the uer utility. Firtly, we will preent the ytem operation and running, with ome reen hot of the appliation. Seondly we will deribe the ytem uing a imulator whih provide the onumption and tate, randomly generated of 1,000 virtual uer to validate the ytem working in a real enario. For the firt objetive, Fig. 7 how different reen hot about uer' appliation operation. One the uer i logged in, a it wa explained in Setion 5, the ommon ontent delivered by the broadater i hown on the uer appliation' player (hot no. 1). Then, when an advertiement break ome aording to the next time break given by the erver, uer' appliation hange the regular advertiing to a peronalied one. Shot no. 2 how a ommon pot, while hot no. 3 how another mobile SI 1Í Si 3,, 3 > o u o O a 13 -i S U -i = 3 '> B o o 5 ÍH ^M 3 i "3 -i in ^ 3 H g <N O ^H 3, tí ^ 3 P B ^ O ^H "3, S3 P Bo O ^H 3. 5 o Pá 3 S -8 ^ tí >-i tí 3 B a O 00 3, 5 o =1 > Pá 3 S ^ tí ^ 3 P B - *> 5 S3 Pá 3 a 2 73 ""> 5. o ^ i Pá 3 «3 & B Pá 3 S - ^ S3 Pa 3 S 3. Pá 3 & 5 (^ <D -2 Í <D S S 3 tí 2 <ÍH X 1^ X a-, o X 1^ X O 1^ 1^ ^ o X o o o o o O 1^ 1^ > <N m ** ii Í-I Í-I i- OJ OJ OJ OJ *a- *a U O O O O

13 terminal with the peronalied ontent diplayed, both aquired in the ame time intant. Finally, in order to ynhronie thi hange and to make the ytem operation eamle for uer, the hot no. 4 how a typial advertiement bumper, whih prevent uer from wathing a blak reen during the hange. The ytem work aording to the planned heme, and wa teted in everal terminal running Window Mobile 6.0 a the operating ytem. The player ued are VL Media Player for the ommon flow and Window Media Player for the peronalied ontent. The eletion of the uer interfae preented in Fig. 7 wa done uing the ommon omponent of the uer interfae whih the operative ytem for mobile devie provide for video wathing. In Fig. 7 we ued the media player interfae to offer the uer the ame experiene a with other media ervie. Seondly we teted the uer eletion proe and the laifiation, lutering and targeting innovation whih we developed in Setion 4 in our ytem. The teting of the lutering and laifiation, due to the omplexity of olleting a high number of real uer, it wa performed with a high number (1,000) of virtual uer for whih we imulated their behaviour when onuming ontent. To reate the imulated uer, we took the publi audiene meaurement data from the TV ontent onumption in broadating platform in Spain in 2008, with a further elaboration, whih over around 8,000 houehold. For etablihing the total onumption per ontent ategory, we have taken the data from thi oure and laified them into a total number of 8 ategorie. Then, we ued thi information to elaborate the total onumption perentage whih will be ditributed tatitially. The onumption wa imulated along 2 week for different ontent ategorie, and a we deribed, with a MGT of 8 ontent ategorie (Table 2). The imulation emulated thi ontent onumption per ategory taking into aount that the emulated uer will have different onumption pattern. We need in addition to onider the tratifiation of the uer in different age with different onumption pattern. Depending on everal oioeonomi fator, uh a age, ity of reidene, et. the total audiene an be tratified. For example, taking a bae the data from audiene meaurement in Spain, modified to be adapted to the evolution until nowaday, we ue: Thi mean that the imulation hould omply with the following retrition, in addition to the general ondition expreed: a) The average of the ditribution of the ontent onumed hould be ompliant with the total ontent ategory perentage. b) The average of the number of minute onumed per day per age group hould be ompliant with the Table 3. ) The ditribution of onumption perentage per age group hould be ompliant with the Table. d) The total uer imulated hould be ompliant with the Table 3 and the total number of uer i 1,000. e) A we are going to imulate 2 week of one regular month, and the ditribution of the ontent onumption i tatitially ditributed uing the imulator, mall differene may reult. A a reult, we hould divide the different age group depending on the ontent onumption average, whih are not equally ditributed. The onumption per age group i Fig. 8 lutering repreentation (uing Fuzzy -mean algorithm) in 3D of the data

14 harateried aording to onumption tendenie, whih may vary depending on the ountry, oial group, et. In our imulator we have ued the following ditribution to tart the imulator, generating uing n-dimenional normal ditribution to reate the uer profile regarding the onumption (Table 4): For the determination of the Uer Profile regarding the ubjetive ontent appreiation (U p ) we need to haraterie L(U). We imulated L(U) individually and randomly uing a 8-dimenional uniform random variable within a ale from 0 to 1. We defined a number of 5 luter (although it an be left open, indiating the maximum ditane), leaving the algorithm open. The reult are a luter memberhip funtion matrix, indiating the degree of belongingne of eah uer to luter. After running the algorithm, we have eleted the maximum of thi memberhip matrix, reulting in a lutering of the uer in ommunitie. We eleted randomly a an example, 14 virtual uer in 5 luter, and the reult are the following, a hown in Table 5: The memberhip funtion an be ued afterward to provide additional information about the uer and their ommunitie' belongingne, a they an be member of one or everal luter, aording to the eleted threhold value. For example uer 5 may be member of the luter 2 or 3. Figure depit the lutering proe in a 3D repreentation. One the uer have been aigned to luter, the proper advertiement that better fit to their profile an be delivered. For thi to happen, the advertier hould provide their material already labelled by ontent ategorie and luter, aording to the obtained profile. The reult of our imulation are the targeting of eah advertiement to the member of the luter whih are loer to the advertiement ontent ategory. After heking the reult of the imulation, the advertiement whih were aigned to peifi onumption pattern were eay to being laified to the luter. 7 onluion A new ytem for mobile advertiement peronaliation ha been preented. Thi ytem repreent an effiient and ueful olution whih an produe a new buine model for the mobile viual advertiing market. Furthermore, it an be applied to other tranmiion mehanim that may allow for ontent utomiation, uh a IPTV, digital terretrial, atellite or able televiion (uing the bidiretional return hannel and the Set-top- box middleware). In thi ene, the emerging Hybrid Broadat- Broadband TV ytem (Hbb-TV) may learly benefit from our propoal, a they ombine broadband and broadat media delivery. Referene 1. Digital Video Broadating - Handheld (DVB-H) web-ite www. dvb-h.org 2. Phatak DS, Mulvaney R (2002) lutering for peronalized mobile web uage. 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