Icete content-based filtering with applications on tv viewing data

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Recommendation systems provide recommendation based on information about users’ preferences. Information Filtering is used by recommendation systems so as information can be processed and suggested to users; and Content-Based Filtering is an Information Filtering approach very used in recommendation systems. Content-Based Filtering analyses the correlation of items content with the user’s profile, suggesting relevant items and putting away irrelevant items. Recommendation systems, which are very much used on the Internet, have been studied in order to be used on Digital TV context, and there already are several works in this sense. As they are used on the Internet, recommendation systems can be used in Digital TV in order to recommend TV programs, publicity and advertisement and also the electronic commerce. Thus, within Digital TV context, the items can be programs, advertisements and the products to be sold; and using Content-Based Filtering in the recommendation programs, for instance, these programs’ contents can be correlated with the user’s preferences, which in this scenario, are the type of program one wants to watch. This paper presents the studies accomplished with Content-Based Filtering with application on Digital TV data. The survey aims at observing and evaluating how some filtering techniques based on content can be used in recommendation systems in Digital TV context

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Icete content-based filtering with applications on tv viewing data

  1. 1. CONTENT-BASED FILTERING WITH APPLICATION ON TV VIEWING DATA Preparation of Camera-Ready Contributions to INSTICC Proceedings Elaine Cecília Gatto, Sergio Donizetti Zorzo Department of Computer Science, Federal University of São Carlos, Rodovia Washington Luís, Km 235, PO Box 676, São Carlos, Brazil elaine_gatto@dc.ufscar.br, zorzo@dc.ufscar.brKeywords: Personalization, Recommendation, Information Filtering, Brazilian Digital TV, Content-Based Filtering, Recommendation System, Collaborative Filtering, Hybrid Filtering, One-Seg, Full-Seg, Middleware Ginga, Cosine, Apriori.Abstract: Recommendation systems provide recommendation based on information about users’ preferences. Information Filtering is used by recommendation systems so as information can be processed and suggested to users; and Content-Based Filtering is an Information Filtering approach very used in recommendation systems. Content-Based Filtering analyses the correlation of items content with the user’s profile, suggesting relevant items and putting away irrelevant items. Recommendation systems, which are very much used on the Internet, have been studied in order to be used on Digital TV context, and there already are several works in this sense. As they are used on the Internet, recommendation systems can be used in Digital TV in order to recommend TV programs, publicity and advertisement and also the electronic commerce. Thus, within Digital TV context, the items can be programs, advertisements and the products to be sold; and using Content-Based Filtering in the recommendation programs, for instance, these programs’ contents can be correlated with the user’s preferences, which in this scenario, are the type of program one wants to watch. This paper presents the studies accomplished with Content-Based Filtering with application on Digital TV data. The survey aims at observing and evaluating how some filtering techniques based on content can be used in recommendation systems in Digital TV context.1 INTRODUCTION (Bozios et al, 2001), (Gutta et al, 2000), (Das and Horst, 1998), among others. Digital TV implementation in Brazil provides Recommendation systems can contribute to anew markets which can be explored. Well-succeeded better use of Digital TV in residences, in groups ortechnologies as those in Web environment, for individually, in a cell phone, for example. Theseexample, can be applied in Digital TV domain and systems can help the user to choose the program,achieve the same success. avoiding waste of time and of course, suggesting to The interaction either through the remote control the user programs which really interest him.or the cell phone keyboard etc by the user today, will Moreover, recommendation systems can be appliedallow many applications to be carried to this to publicity and advertisement on Digital TV, asenvironment. well as in the T-Commerce. One of the areas which has been extensively This paper is structured as follows: Section 1studied and is well-succeeded in the Web is that of provides a brief introduction to the survey, Section 2personalization. There are some surveys concerning deals briefly with recommendation systems and itsrecommendation systems for Digital TV as for techniques; Section 3 quickly describes Brazilianexample (Ávila, 2010), (Lucas, 2009), (Uribe, current conditions related to Digital TV; Section 42009), (Solla et al, 2008), (Bar et al, 2008), presents tests performed with TV viewing data;(Einarsson, 2007), (Chorianopoulus, 2007), (Choi, Section 5 presents the outcomes from the tests andKoh and Lee, 2007), (Yu et al, 2006), (Silva, 2005), Section 6 concludes the paper.
  2. 2. 2 RECOMMENDER SYSTEMS Association rules interconnect objects trying to present characteristics and tendencies. AssociationIn a typical recommendation system, the users findings must evidence either common associationsprovide recommendation as inputs which are then or uncommon associations.added and directed to proper receivers. (Resnick, Apriori algoruthm is frequently used to mine1997) association rules. Apriori operates with a high With the first articles on collaborative filtering number of attributes, creating several combinationsaround the 90’s, recommendation systems became among them and performing consecutive search inan important area of research. Recommendation the whole database, keeping a great performance insystems comprise several technologies as cognitive terms of time spent in the processing.science, approximation theory, information The algorithim tries to find all the relevantrecovery, forecast theories, among others, and can association rules between the items, which have thebe applied to several domains. X format (precedent) ==> Y (consequent). If x% of The recommendation problem in its most transactions which have X also have Y, so x%common form is reduced to a way of evaluating represents the confidence factor (power ofitems which were not seen by a user. Evaluation of confidence of the rule). The support factor is anon-evaluated items can be estimated in many measure corresponding to x% of X and Y occurancedifferent ways, frequently classified according to its simultaneously upon the total of registersapproach to classification estimate. In Sections 2.1, (frequency). (Witten, 2005)2.2 and 2.3, recommendation systems classificationis presented. (Adomavicius, 2005) 2.2.2 Cossine Cosine is a similarity measure, a metrics which can2.1 Content-Based Filtering be applied to discover if an item has correlation or not with the user profile. In many recommendationContent-Based Filtering (CBF) uses the content systems for the Web, the applied techniques use theattributes to describe the content of the items and evaluation performed by the users, for the productsthen calculate the similarity. This approach does not consumed to calculate the similarity.depend on other users’ evaluation about the items. In our context, this evaluation by the user is not(Einarsson, 2007) possible yet, therefore, we used the time a person CBF is an information recovery technique spent watching the program as an evaluation. In thewhich bases its forecast on the fact that previous same way we found an alternative, virtual storespreferences of the users are reliable indicators for which do not require users’ evaluation for itsfuture behavior. (Chorianopoulos, 2007) products can consider “consumed product” and not In order to formulate recommendations, a “non-consumed product” as an evaluation.variety of algorithms has been proposed to evaluate A binary vector is a set of two elements, x and y.the content of documents and find regularities. Some In an n-dimensional space, where n is the number ofof these algorithms operate with classification items of the vector, it is possible; therefore, calculateknowledge and others operate with the problem of the cosine between the vectors, thus evaluating theregression. (Pazani, 1999) similarity between the user profile and its history. Some of the problems and limitations found The similarity is high when the cosine value is high.in systems using CBF are super specialization, the The cosine formula is presented below:problem of the new user and the analyses of limitedcontent. The following 2.2.1 and 2.2.2 subsections    ( p.e )describe two techniques which can be used in CBF cos( p, e )    (1)and which were applied in our survey. | p |.| e |(Adomavicius, 2005) Where is the profile vector and is the EPG2.2.1 Apriori vector. The symbol means the profile vector standard and the symbol the EPG vector standard . The algorithms of association techniques identify (Torres, 2004, 2009)associations between register of data related in someway. The major premise finds elements whichrequire the presence of others in a same transaction,aiming at determining what is related.
  3. 3. 2.2 Collaborative Filtering Table 1: Number of Individuals per Residence. Residence 1 2 3 4 5 6Collaborative Filtering (CF) is a technique which Individuals 2 3 3 2 2 3uses the similarity between users in order to generate TVs 1 1 2 2 1 2recommendations and it first came to light in the90’s, with Tapestry system, different from CBF Table 2: Social-economic characteristics at Residences 1,which calculates the similarity between the items. 2 and 3. CF stores the users’ evaluation about each itemand based on this information, finds people with Residence 1 2 3similar profile, the so-called nearest neighbors, who Social DE C Care then gathered and the products with high Classevaluations by neighbors are recommended. Residence 1 2 3 Age of the(Balabanovic, 1997; Torres, 2004) 44 45 39 hostess Even solving some CBF problems, CF introduce Level ofothers as the problem of the new user, the problem Incomplete Incomplete Incomplete education ofof the new item and the sparcity. Primary High High the owner of School School School the house2.3 Hybrid Filtering Individual 1 Female Female Female genderHybrid filtering mixes CBF and CF in a sole system, Individual 1 8 48 40improving recommendation offered to user and thus, ageseeks to solve some of the problems introduced by Individual 2 Female Male Male genderboth techniques. Individual 2 This way, recommendation methods in this - 17 13 agecategory can be matched in many ways: a) CF Individual 3sequentially processed after CBF; CBF sequentially - Female Female genderprocessed after CF and CBF parallelly processed Individual 3with the CF. (Einarsson, 2007; Adomavicius, 2005) - - - age Table 3: Social-economic characteristics in Residences 4,3 BRAZILIAN DTV 5 and 6. Residence 4 5 6 Since December, 2007 in Brazil, the implantation Social AB C ABof Brazilian Digital TV has been innovating by Classmatching Japanese technology with technology Age of the 32 60 36developed by Brazilian universities. hostess Besides having all the advantages of Japanese Level of Complete Complete Complete education ofsystem, Brazilian system counts on Ginga High High High the owner ofMiddleware which uses LUA, NCL and Java School School School the houselanguages, totally developed by national researches. Individual 1 Peru, Argentina, Chile and Venezuela chose the Female Female Female genderNipo-Brazilian standard of Digital TV which is Individual 1already part of UIT. Nipo-Brazilian standard offers 30 77 38 agequality of image and sound, mobility, portability, Individual 2 Male Male Maleflexible interactivity; it is free of royalties and genderprovides the development of commercial, playful, Individual 2 - - 14informative, governmental, social inclusion ageapplications, among others. (SBTVD Forum, 2009) Individual 3 - - Male gender The standard (ABNT NBR 1564, 2008) defines Individual 3the set of essential functionalities required from - - - agereception devices of 13-segment digital television –Full-seg – as well as from one-segment – One-seg –designated to receive signals in fix, mobile andportable modality.
  4. 4. Still according to this standard, full-seg presents the names of broadcasting stations with theclassification is applicable to digital converters – set- number of programs and genres transmitted.top box – and to 13-segment receptors integrated tothe viewing screen, but not exclusive to these; andone-seg classification is designated to portable-typereceptors – handheld – specially recommended forsmaller screens, commonly up to 17,80 inches. The content can be then displayed in manydifferent devices, as well as diversified services canalso be formulated for each one, allowing thecreating of new business models and newopportunities for professionals. Ginga is the name of the middleware developedby researches performed by Telemedia laboratoriesat PUC-Rio and LAViD at UFPB. The middlewareis divided in Ginga-NCL/LUA, corresponding to thedeclarative part and Ginga-J, the imperative part.(GINGA, 2010)4 TESTS Figure 1: Types of data composing EPG.So as the test could be performed, data Table 4: Number of broadcasting stations, programs and genres in EPG.corresponding to TV viewing and from the TV guidewere used. This data was provided by IBOPE. The Broadcasting Programs Genres/Subgenrescharacteristics of this data and the performed tests stationsare detailed in the following subsections. 1 Bandeirantes 70 23 2 Gazeta 40 104.1 Characteristics of Residence 3 Globo 76 18 4 MTV 149 12 5 RBI TV 46 12Data provided by IBOPE correspond to 15-day 6 Record 42 15monitoring at 6 Brazilian residences with Open TV 7 Record News 100 10programs. 8 Rede TV 67 20 These residences were monitored minute-to- 9 SBT 61 15minute, as well as each individual was monitored 10 TV Cultura 167 22separately. Table 1 shows the number of individualsand TVs by residence, Table 2 presents the social- 4.2.2 User Historyeconomic information of residences 1, 2 and 3; andTable 3 deals with residences 4, 5 and 6. Users’ viewing history is necessary in order to discover their preferences.4.2 Characteristics of Date In the Digital TV context we are considering, this data are collected and stored implicitly.Data used for these tests undergone a manual Figure 2 presents the composition of data andprocess of adaptation. For each of the algorithms Table 3 presents a sample of data in the viewingused, it was necessary a pre manual processing so as history.they could be correctly analyzed and used.Subsections 4.2.1 and 4.2.2 detail the composition of Table 5: Amostra do histórico de usuário.these data. Field Content startSyntonization 2008-03-05 09:28:004.2.1 EPG endSyntonization 2008-03-05 12:59:00 durationSyntonization 03:31:00EPG provided by IBOPE corresponds to the 15-day Date 2008-03-05schedule of 10 broadcasting stations. Figure 1 shows timeStart 09:28:00the types of data which composes EPG and Table 4 timeEnd 12:59:00
  5. 5. duration 211 marked in the matrix with the value of 1 and the periodSyntonization morning remaining is marked with the value of 0. This has day of the week Wednesday been done for all programs composing EPG. Programcode 003217 After this, a table called “profile” was created Programname HOJE EM DIA which stores the user profile found consulting SQL, Broadcastingstationcode 006 which is showed in a simple way, in Figure 3 below. Broadcastingstationname Record The “profile” table is presented in Figure 4. Genre 0x6 Genredescriber Variety Select avg(ded1), avg(dee1), …, Subgenre 0X0F avg(vs1) Subgenredescriber Others from (select domicilio.nomePrograma, genreSubgenre 0x6_0X0F domicilio.descritorGeneroSubgenero, GeneroSubgenerodescriber Variety_Others duracao*DED as ded1, duracao*DEE as dee1, …, duracao*VS as vs1 from domicilio, matrizepg where domicilio.nomePrograma = matrizepg.nomePrograma order by duracao desc) as result; After that, a variable was set: set @profilenorm= (select sqrt(ded1*ded1+dee1*dee1+ … +vs1*vs1)from profile); Figure 2: Types of data composing the user history.4.3 Methodology In order to carry out the tests, we simulated thegeneration of recommendations and profile for eachresidence, using two different techniques, Aprioriand Cosine. Figure 3: Fields added to EPG generating EPG For the Cosine, we used MySql databank. For Matrix.each new day, we inserted in the databankcorrespondent to the viewings and then, we appliedthe recommendation technique, we discovered theprofile and which program to recommend. The process occurs in an interactive systematicway. First, data corresponding to the first day ofmonitoring is inserted in the databank and the EPGmatrix is created, that is, EPG is transformed in amatrix containing, besides the data in Figure 2, theGenres and Subgenres of each program separately,as presented in Figure 3. Each abbreviation indicatesone genre/subgenre. If a program belongs to one or moregenre/subgenre, as for example, sport and Figure 4: Table Profile.documentary journalism, these genres/subgenres are
  6. 6. And finally, the final result with the following For the case of Cosine, the existence of programsconsult: seen by the user in the following day in the results based in the previous day was verified. This was theselect nomePrograma, best way for the evaluation, for the evaluationdescritorGeneroSubgenero, cannot be done directly with the users, however, it isdot/(@profilenorm*norm) as cos, possible to know what the user has seen before andDED, DEE, …, VSfrom (select nomePrograma, after each step.descritorGeneroSubgenero, Thus, two additional tables were created; one insqrt(DED*DED+DEE*DEE+…+VS*VS) as norm, order to store the result of the cosine and the other toDED*ded1+DEE*dee1+…+VS*vs1) as dot, store only what was seen in the following day. TheseDED, DEE, …, VS tables were called “recommend” andfrom matrizepg, profile) as normdot “residence_test” and the following SQL consult wasgroup by nomePrograma used to evaluate:order by cos asc; select r.*, dt.nomePrograma, Thus, the programs which can be recommended dt.descritorGeneroSubgeneroto the user according to his profile were found. The from recomenda r, domicilio_teste dtsame thing can be done to fid only the where dt.nomePrograma = r.nomeProgramagenres/subgenres. group by r.nomePrograma For Apriori, Weka tool was used having as order by cos desc;parameters minima support o,1, reliance 0,9, classattribute index -1, total of 20 rules and enabled car This way it is possible to discover if in theproviding the mining of the association rules instead following day, the individual watched some programgeneral rules of association. which is in the “recommend” table and to verify the StringToNominal and NumericToNominal value of its cosine. If this value is near 1, then weconversion filters were also applied in some fields, can say that the cosine gave a right forecast.generating the rules and saving the outputs. Below is A behavior in which 5 recommendations werea small sample of these rules: offered was simulated. If any of these 5 recommendations were seen on the next day and if1.genero=0x62==>descritor=Variedade_Out its cosine is near 1, so it is assumed that theros2conf:(1) recommendation was accepted. Figures 5 to 10 present the percentage of right2.descGenero=Variedade2==>descritor=Var cosine, during 15 days of monitoring in eachiedade_Outros2conf:(1) residence, according to our methodology of3.subGenero=0X0F2==>descritor=Variedade simulation. Figure 11 presents the average of all_Outros2conf:(1) residences. Graphics were generated with the following4.descSubGenero=Outros2==>descritor=Var formula:iedade_Outros2conf:(1)5.genSubg=0x6_0X0F2==>descritor=Varieda Number of Hits (0 a 5)de_Outros2conf:(1) Percentage= (2) Number of recommendations (5)6.dia=2008-03-05genero=0x62==>descritor=Variedade_Outros2conf:(1) For the case of Apriori, it was possible to verify if the individual had seen some of the genres/subgenres identified in the rules in the following day. These are a little different approach.5 RESULTS While in Cosine the operation was direct with the names of the programs, in Apriori, the genres and itsAfter describing the methodology used, this sections respective subgenres were used.presents the results. The techniques were applied; The same methodology to simulate the cosinethe results were evaluated and verified to see if was used for the Apriori. Figures from 12 to 17correct recommendation was being generated. present the hit percentage of Apriori, during 15 days of monitoring in each residence, according to the
  7. 7. simulation methodology. Figure 18 presents theaverage of all residences and Figure 19 presents acomparison between the averages of each one of thetechniques for all the residences. Figura 8: Percentage of cosine hits, during 15 days in residence 4. Figura 5: Percentage of cosine hits, during 15 days in residence 1. Figura 9: Percentage of cosine hits, during 15 days in residence 5. Figura 6: Percentage of cosine hits, during 15 days in residence 2. Figura 10: Percentage of cosine hits, during 15 days in residence 6. Figura 7: Percentage of cosine hits, during 15 days in residence 3.
  8. 8. Figura 11: Average of the Cosine in all residences. Figura 14: Percentage of Apriori hits, during 15 days, in residence 3.Figura 12: Percentage of Apriori hits, during 15 days, Figura 15: Percentage of Apriori hits, during 15 days, in residence 1. in residence 4.Figura 13: Percentage of Apriori hits, during 15 days, Figura 16: Percentage of Apriori hits, during 15 days, in residence 2. in residence 5.
  9. 9. Table 6: Difference between Apriori and Cosine. Residence 1 19% Residence 2 8% Residence 3 5% Residence 4 16% Residence 5 8% Residence 6 28% However, apriori provided other kinds of information which are difficult to collect with the cosine, concerning the users behavior in each Figura 17: Percentage of Apriori hits, during 15 days, residence. While cosine is focused to select the in residence 6. programs to be recommended according to the profile generated also by the cosine, it is possible to use apriori to find out other characteristics and thus improve the quality of recommendations. Table 7 present some of these characteristics. This table presents the day of the week, the period of the day, the genre/subgenre and the broadcasting station watched by each one in the residences. This information is independent, for example, a residence might have watched soap opera, but this soap opera is not necessarily from the most watched broadcasting station Table 7: Characteristics found out with apriori. Figura 18: Average of the Apriori in all residences. Period Broadca Day of the Genre/ R of the sting week Subgenre day station Afterno Variety_other 1 Thursday record on s Wednesda Soap Opera_ 2 Evening Globo y Soap Opera children_child 3 Thursday Evening Globo ren Soap Opera_ 4 Sunday Evening Record Soap Opera Journalism_ne 5 Friday Evening Globo wcast Soap Opera_ 6 Friday Evening Record Soap Opera Figura 19: Comparison of the hits average between Apriori and the Cosine in all residences. It could also be seen that the apriori used in these data tend to be super-specialized, always finding the Certainly, the difference between the techniques same genres and subgenres to recommend. Thisis visible and presented in Table 6. It is important to shows that it is necessary operate together with otherpoint out that although the methodology is the same techniques to create the surprise recommendation tofor both, the techniques were observed and analyzed the user, particularly in this case.by different point of views, the cosine directed to the The data we have are simple and do not havename of the program and the apriori for details as synopsis, name of the actors, directors,genres/subgenres. sport categories etc. It is expected that, in Brazilian Digital TV, these attributes are present, increasing the probabilities of recommending not only the obvious but also something new that the user would probably watch.
  10. 10. 6 CONCLUSION Proceedings of the Technological Forecasting & Social Change, p. 1043-1053, 2007. Chorianopoulos, K. Personalized and mobile digital TV According to the studies presented herein, it is applications. In Proceedings of the Multimedia Toolspossible to apply FBC in TV viewing data and thus, and Aplications, p. 1- 10, vol.36, 27 January 2007.it can also be applied for developing Cristo, M. Sistemas de Recomendação, Métodos erecommendation systems for Digital TV, Avaliação. 81 slides. 2009.particularly in Brazil. Das, D. and ter Horst, H. Recommder Systems for TV. In Two different techniques were used in the same Proceedings of 15 th AAAI Conference, Madison,data and it was possible to note that, despite the Wisconsin, July 1998. Einarsson, O. P. Content Personalization for Mobile TVdifferences among them, both can be used in order Combining Content-Based and Collavorative Filtering.to find out the profile and to provide Master Thesis. Center for Information andrecommendations, as well as they can be used Communication Technologies. Technical Univesity oftogether to provide even better recommendations. Denmark. August 22, 2007. There are also other FBC and FC techniques Fórum SBTVD. TV digital nipo-brasileira agora éwhich will be tested in future works, together with oficialmente referência mundial. Available in:hybrid techniques. More detailed data is also <http://www.forumsbtvd.org.br/materias.asp?id=238>,expected as synopsis, indicative classification, Acess in January 2010, 09h00.among others, in order to improve the quality of Ginga. Available in: <http://www.ginga.org.br/>, Acess in January 2010. http://www.ginga.org.br/recommendations in TV viewing domain. Gutta, S. et al. TV Content Recommender System. In Proceedings of the 17th National Conference of AAAI, Austin, TX, 2000.ACKNOWLEDGEMENTS Lucas, A. Personalização para Televisão Digital utilizando a estratégia de Sistema de Recomendação para ambientes multiusuário. 103 pages. 2009.We thank IBOPE for providing real data about the Pazzani, M. J. A framework for Collaborative, Content-electronic program guide and also the viewer’s Based and Demographic Filtering. Artificialbehavior data from March, 05, 2008 to March, 19, Intelligence Review, p. 393-408, December 1999.2008. Resnick, P.; Varian, H. R. Recommender Systems. Communications of the ACM, New York, vol. 40, n. 3, p. 77-87, March 1997. Silva, F. S. Personalização de Conteúdo na TVDI AtravésREFERENCES de um Sistema de Recomendação Personalizada de Programas de TV (SRPTV). In III Fórum deABNT NBR 15604:2008. Televisão Digital Terreste Oportunidades em Televisão Digital Interativa, Poços Receptores. 07/04/2008. 68 pages. de Caldas, Minas Gerais, Brasil, 2005.Ávila, P. M. Recommender TV: Suporte ao Solla, A. G. et al. ZapTV: Personalized User-Generated Desenvolvimento de Aplicações de Recomendação Content for Handheld Devices in DVB-H Mobile para o Sistema Brasileiro de TV Digital. Dissertação Newtorks. In Proceedings 6th European Interactive de Mestrado. 90 pages, 2010. TV Conference, p.193-203, Salzburg, Áustria, 03-Adomavicius, G.; Tuzhilin, A. Towards the Next 04/07/2008. Generation of Recommenders Systems: A Survey of Torres, R. Personalização na Internet. Editora Novatec, the State-of-the-Art and Possible Extensions. IEEE 157 pages, São Paulo, Brazil, 2004. Transactions on Knowledge and Data Engineering, Uribe, S. et al. Mobile TV Targeted Advertisement and vol. 17, Issue 6, p. 734-749, June 2005. Content Personalization. In 16th InternationalBalabanovic, M. ; Shohan, Y. Content-Based, Workshop Conference on Systems, Signals and Image Collaborative Recommendation. Communications of Processing, Chalkida, Greece, 18-19/06/2009. the ACM, New York, vol. 40, n.3, p. 66-72, March Witten, I. H.; Frank, E. Data Mining: Practical Machine 1997. Learning Tools and Techniques, 2nd Edition, MorganBär, A. et al. A Lightweight Mobile TV Recommender: Kaufmann, 525 pages, June 2005. Towards a One-Click-to-Watch Experience. In Yu, Z. et al. TV program recommendation for multiple Proceedings 6th European Interactive TV Conference, viewers based on user profile merging. In Proceedings p.142-147, Salzburg, Áustria, 03-04/07/2008. of the User Model User-Adap Inter, p. 63-82, 2006.Bozios, T. et al. Advanced Techniques for Personalized Advertising in a Digital TV Environment: The iMedia System. In Proceedings of the eBusiness and eWork Conference, p. 1025-1031, IOS press, 2001.Choi, J. Y.; Koh, D.; Lee, J. Ex-ante simulation of mobile TV market based on consumers’ preference data. In

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