BIPoDi TVR: Brazilian Interactive Portable Digital TV
Recommendation System
Elaine Cecília Gatto

Sergio Donizetti Zorzo

In Brazil, the quantity of cell phones is much bigger than the
quantity of TV sets, what can quickly stimulate the use of ...
responsible for formatting the data providing a safe and adequate
communication among the modules.
Concluding, the system ...
The BIPoDi TVR Trigger is responsible for starting and finishing
the data processing of the system. The BIPoDi TVR Capture...
Table 4. Identifying the fields in TXT files


Station code




no people


no TVs









Picture 7. Characteristics of ...





















Picture 8. Spreadsheet relati...
WKI_ID=3883. Access in August 18, 2009.
[4] Televisão digital terr...
Upcoming SlideShare
Loading in …5

BIPODITVR: brazilian interactive portable digital tv recommendation system


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

BIPODITVR: brazilian interactive portable digital tv recommendation system

  1. 1. BIPoDi TVR: Brazilian Interactive Portable Digital TV Recommendation System Elaine Cecília Gatto Sergio Donizetti Zorzo Universidade Federal de São Carlos Rodovia Washington Luís, Km 235 Caixa Postal 676, CEP 13565-905 Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil Universidade Federal de São Carlos Rodovia Washington Luís, Km 235 Caixa Postal 676, CEP 13565-905 Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil ABSTRACT Using the Brazilian digital television system, the possibility of offering new services and programs, and consequently more available content, will make it difficult for the users to select their favorite programs. The Recommendation Systems become a tool to solve these difficulties and they are able to improve interactivity between the user and the digital television filtering information filtering and personalizing the content offer. This paper describes a recommendation system for Brazilian interactive portable digital television focused on the cell phone which makes this functionality possible and creates TV program recommendation according to user TV programs preferences when using television in the cell phone. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – Selection Process , Information Filtering; H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems. General Terms Algorithms, Desgin. some kind of interactivity for the portable digital television has been already offered in some countries which have this service, for example, voting in programs, shopping advertisement, electronic programming guide, etc. The electronic programming guide [3, 4, 5] helps the user to find the TV program he wants to watch. However, the increase of content in electronic programming guide is unavoidable with the inclusion of new channels and, due to the great quantity of information; the user starts to find difficulties in choosing programs, resulting in waste of time. The electronic programming guide, overloaded with information, does not meet the user necessity, as it does not take their preferences in account, and the lists presentation on the screen becomes boring because they are long. For the portable TV users, this situation is even more aggravating. The presentation of long programming lists on a reduced screen will bring even more difficulties. So, the interactive portable digital television users focus on the current lack of the device resources and do not want to waste their time selecting programs. Different from using digital television in houses where it is common to change channels frequently and navigate the electronic programming guide, interactive portable digital television takes considerable time and energy. [6, 7] Keywords Middleware Ginga, Mobile TV, Multimedia, Personalization, Profiling, Recommendation System. 1. INTRODUCTION New services, products, contents, channels and business models have been created with the digital television. The Brazilian digital television system [1, 2] allows permanent and portable reception, high audio and video quality and interactivity, creating different contents for permanent and portable interactive digital television users. The interactive portable digital television shares in only one device, internet, TV, cell phone, and the TV signs for these devices are already available in many Brazilian cities. Nowadays, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SAC’10, March 22-26, 2010, Sierre, Switzerland. Copyright 2010 ACM 978-1-60558-638-0/10/03…$10.00. Table 1. Comparison between permanent and portable digital television in Brazil Permanent Set-top-box Portable TV sets with built-in converter PDAs cell phones, Mini-TVs, Smartphone’s, Blackberries, Receptors for automobiles Many users One user Screen bigger than 30 inches Screen bigger than 10 inches Permanent place Anywhere Longer viewing time Shorter viewing time No Return Channel defined Return channel from the cell net Reference implementation of the available middleware Reference implementation of the non-available middleware
  2. 2. In Brazil, the quantity of cell phones is much bigger than the quantity of TV sets, what can quickly stimulate the use of digital television in this kind of device when theses cell phones with digital TV become more accessible to the population. [8, 9] The main advantage of the portable digital television is that the user can use it in any place and at any time. On the other hand, the advantage of permanent digital television is watching the programs at home for a longer time. Table 1 shows a comparison between the permanent and portable digital television in Brazil. The users of these devices need private attention due to the current characteristics of this environment like processing power, storage capacity and battery. In order to enjoy all the potential provided by the interactive portable digital television, a software is necessary to link the hardware, the operational system and the digital television interactive applications. Such software is the middleware called Ginga in Brazil [10, 11]. Ginga middleware allows the construction of declarative and procedural applications using Ginga-NCL (Nested Context Language) [12] and Ginga-J (Java) [13] respectively. The proposed model in this work used a Ginga-NCL middleware reference implementation. NCL [14] is a declarative language used to authorize hypermedia documents and it was developed based on a conceptual model which focuses on representing and treating hypermedia documents. NCL is Ginga-NCL official language and it can be used in portable devices. Finally, the main goal of this work is to develop a recommendation system for Brazilian interactive portable digital television in order to recommend TV programs according to the user profile. This paper is divided in: section 1 presenting the context of the work, section 2 presenting some correlated works, section 3 presenting the recommendation system for Brazilian interactive portable digital television, as well as its characteristics, architecture and implementation, section 4 presenting the results and section 5 the conclusion. 2. CORRELATED WORKS There are many recommendation systems for set-top-boxes allowing personalization services. More information about these systems can be found in [15, 16, 17]. Developing recommendation systems for cell phones with television is a current area of research. Three works which applies recommendation techniques for interactive portable digital television are presented bellow. In [7] a recommendation system for the DVB-H (Digital Video Broadcast – Handheld) standard [18] was developed according to OMA-BCAST (Open Mobile Alliance-Mobile Broadcast Services Enabler Suite) [19]. The authors have identified some requirements for the recommendation systems dedicated to this environment as scalability, response latency, flexibility for current standards of transmission, user privacy protection, among others. The recommendation system is in the category of systems with filtering based on content using text mining. It uses a simple interface with the user and accepts natural language as text entry as well as four values reflecting the user preferences for comedy, action, horror and eroticism. The recommendation in this system occurs as follows: first, the texts are extracted, next, the emotion in the text is analyzed and the distances between the topics are calculated. For each entry, an index is calculated and a list of programs organized by this index is recaptured. The ZapTV [20] developed for DVB-H standard allows the user to create his own content, offering aggregated value services as multimodal access (Web and Cell phones), return channel, video note, personalized sharing and distribution of content. Besides the technology provided by DVB-H, ZapTV comprehends other technologies as TV-Anytime [21], Technologies emerging from Web 2.0 [22] and involved in the Semantic Web [23]. The main functionalities of ZapTV include a social net, personalized content broadcasting (implicit or explicit recommendation), thematic channels diffusion planning (agegroup, genre or specific theme), client application and transmission of the electronic programming guide. ZapTV seeks to improve the recommendation using an intelligent personalization mechanism which matches information filtering with semantic logic processes and it was based on the principles of participation and sharing between Web 2.0 users, so that the creation, sharing, classification and note of content make the search for content easier. The main purpose of the system is replacing the ordinary content (Public Broadcasting Station) by a personalized and adjusted one in order to provide more attractive content for the users. The system architecture allows diffusing content both by broadcast, like DVB-H, and by video streaming. There is a server which locates the television flow and the data service; and a content personalized server which is responsible for attributing and managing personal content according to the user preferences and viewing background as well as indicating when a change from the ordinary content to a personalized content must be performed. The user section consists of portable devices which can perform the client application and send back to the server the necessary data helping to set their profile. On the client side there is the Player module which, among other tasks, must execute the contents according to the type of reception available in the device and there is also a module to store the user data collection and personalized content received from the server. There is a module called control which is responsible for performing the player when the user starts the applicative, monitoring, capturing and preparing the user interactions to be sent to the server among other tasks. The last module on the client side is responsible for receiving the personalized content and sending the captured data. The Decissor module, on the server side controls the user profiles in the data bank module, updates the user profile whenever it receives information from the user about the behavior and selects advertisements which have to be sent to the users according to their profiles. The Web Server lodges the web services to manage the system and the contents; and advertisements companies and content providers can add, delete and modify contents, programs and users. There is also a module to control the data flow between the server and the user and other module to the data bank which store the profiles, the data collected from the user behavior and the contents sent by providers. The last module on the server side is
  3. 3. responsible for formatting the data providing a safe and adequate communication among the modules. Concluding, the system requires login/password and when the user accesses the application for the first time, he fills in a form with his preferences in order to generate his profile. After logging in, the user starts watching television either by streaming or by broadcast. tests and the implementation were performed in Ginga-NCL middleware for set-top-box because the implementation for this middleware portable device is not available at the moment. Both works aforementioned provide solutions to the personalization and the information overload in digital television in portable devices. In [7] the recommendation system mechanism applies two techniques: the text mining and filtering based in content besides requiring some data from the; while in [20], the mechanism is more sophisticated, using hybrid information filtering, semantic logic and explicit and implicit user identification. The login is necessary in all of them and the differentials of [24] are the personalized advertisement and the reception of content either by streaming or by broadcast. The work proposed in this article uses a data mining algorithm and implicit collection of the user behavior, which does not require login/password from the user, and was particularly developed for the Brazilian digital television system. However, its model can be applied in other standards. The recommendation systems from previous work are out of the portable device, and this is the most noticeable difference of model proposed in this work. Both systems include, inside existing digital television architecture, its own architecture, like content servers and electronic program guide servers. In this work, the recommendation system is in the portable device and the inclusion of servers in Brazilian interactive portable digital television is not necessary for providing recommendation and, therefore, there is no need of remote communication, avoiding the user to pay by data traffic in the net to receive the recommendation or send data, protecting the user data privacy. 3. BIPODI TVR Picture 1. Context of the system use The processing starts when the user turns on the TV in his cell . phone. The user viewing background data collected until that moment are mined in order to find the user profile.The data resulting from the mining are formatted and the user profile is stored in a data bank, together with date and time of generation. Once the user profile is updated, he can look in the electronic program guide for compatible TV programs with transmission time close to the current time, generating a list with these programs. The list is cleaned and formatted and only the data related to date, time, duration and broadcast station remains generating a new list of programs. The list with the programs includes the recommendations which are also stored in a data base with the date and time of generation. The system proposed in this work aims at making easier the interactive portable digital television user routine by interacting through a simple interface which allows the user to watch his favorite content without spending too much time to find it. The recommendations are presented to the user and those which are required are stored with the viewing background. All the programs the user watched during the period the TV is turned on in the cell phone are stored in the viewing background. BIPoDi TVR (Brazlian Interactive Portable Digital TV Recommender) was projected in order to be executed locally in the cell phone with the digital television functionality. It is also necessary that the device has Ginga-NCL middleware. Picture 1 shows the context to use BIPoDi TVR. The fixed and mobile receptors receive audio, video and data and the middleware is responsible for separating them. All the programs the user watched during the period the TV is turned on in the cell phone are stored in the data base which contains the viewing background. This process is repeated whenever the user turns the TV on. The device must be able to receive the digital television transmission with the help of an internal or external antenna compatible with the standard transmission adopted in Brazil. The user interacts with the television in the cell phone and all the channels viewed during the period of use are stored. The initial propose of BIPoDi TVR considers using the categories and the TV programs start time. As soon as the user turns on the TV in the cell phone, TV programs of his preference with time close to current time are recommended. BIPoDi TVR was developed using Ginga-NCL middleware. The 3.1 Implementation Ginga middleware has a layer for the resident applications responsible for exhibition, other layer for the common core, responsible for offering many services, and a last layer pertinent to the pile protocols. BIPoDi TVR was implemented as an element in Ginga architecture, in the common core layer (Ginga Common Core), as illustrated in Picture 2. BIPoDi TVR is divided in many modules and it was carefully thought, designed and modeled particularly to portable devices, considering its current characteristics in order to meet the requirements of this environment and to agree with the Brazilian rules for portable digital television.
  4. 4. The BIPoDi TVR Trigger is responsible for starting and finishing the data processing of the system. The BIPoDi TVR Capture is responsible for capturing and storing all the programs watched during the period the TV is turned on in the cell phone, as well as the information concerning to the programs like date, time, channel and genre. Picture 3. Modules BIPoDi TVR adequate for this work. There are several algorithms which could . be tested. However, the purpose of this work is not studying, testing and analyzing deeply and systematically the impact of data mining techniques application on devices like cell phones. Picture 2. Recommendation system in Ginga middleware architecture The BIPoDi TVR Mining . is responsible for storing the user profile. This module should also find, in the electronic program guide the programs which can be recommended to the user according to the profile generating results with complete information. The BIPoDi TVR Filter is responsible for filtering the relevant information resulting from the Mining module, formatting them and creating a list of recommendation. The BIPoDi TVR Presentation is responsible for presenting recommendation as well as managing the time the recommendation will be on the screen. The last module, BIPoDi TVR Data Manager, is responsible for deleting the data as soon as they became old. BIPoDi TVR architecture has also data bases (files) to store the user viewing background, the electronic program guide, the user profile and the recommendations. Picture 3 shows the recommendation system architecture. The association techniques algorithms identify associations among data registers related in some way. The basic purpose finds elements involving the presence of others in a same transaction with the aim at establishing what is related. The association rules interconnect items trying to show characteristics and tendencies. Association discoveries should point common and not so common associations. Apriori algorithm is frequently used for mining association rules and can work with a high number of attributes creating many combinations among them and successively searching all data base, keeping an excellent performance relating the processing time. The algorithm tries to find all the relevant association rules among the items which have the X (prior) ==> Y (consequent) shape. If x% of the transaction containing X also contains Y, so x% represents the confidence factor (confidence force of the rule). The support factor corresponds to x% of times that X and Y occur simultaneously on the total of registers (frequency). [25] In order to prove that this algorithm meets the necessary requirements of this work, the tests were performed using data from house 1 and Apriori algorithm of Weka software. Table 2 shows a sample of the rules created by the software. Rule 1 indicates that the Variety/Others describer had 21 occurrences in Record broadcasting station in house 1. 3.2 Mining Algorithm The BIPoDi TVR Mining module uses a mining algorithm. Among the several existent data mining methods and considering the domain specificities of this application, it was possible to verify that the bottom-up method in which the exploring process tries to discover something that is not known yet by extracting only the data standards, as well as the indirect or non supervised knowledge search method and the association tasks are the most Table 2. Sample of rules created by Weka No 1 2 Rules domicilio=1 nomeEmissora=Record descSubGenero=Outros 21 ==> descGenero=Variedade 21 conf:(1) descGenero=Jornalismo 9 ==> domicilio=3 29 conf:(1)
  5. 5. Table 4. Identifying the fields in TXT files Column Content Identification Broadcasting 005 Station code 100PNREX Discarded XXXX 002645 Program Code RELIGIOS Name of the O MAT Program 1 005100PNRE XXXXX 2 002645RELI GIOSO MAT 3 000000 Discarded 4 0000 Discarded 060000 Picture 4. Sample of the TXT files initial layout 5 06000008000 0DIA_05 DIA_0 5 3.3 Tests . In order to test the proposed and implemented system, particularly the mining algorithm, it is necessary to have the user viewing data and also the electronic program guide. This data was provided by IBOPE [26] and was treated through an almost entirely manual process in order to fit the standard format used in Brazilian digital television system and also to be used in Weka mining data software [27] for the tests. The data corresponds to 15 days of programming and monitoring of 6 Brazilian houses. The electronic program guide is composed of 15 TXT files called programming files, one for each day (from March 3 2008 to March 19 2008) with 10 public broadcasting stations starting at 00:00:00 and finishing at 05:59:00 a.m. Picture 4 shows a sample of initial layout of these files and Table 3 shows how this layout was organized. With the first line from Picture 4 as an example, it is possible to identify field according to Table 4. After understanding the files composing the electronic program guide, the data was copied from the programming files to a BrOffice spreadsheet with paste special resource. This resource allowed the data to be exported exactly as it was built in the layout, separating the fields in columns. After exporting, the unnecessary data was discarded. At the moment of exporting, the numeric data lost its format and then it was reformatted according to Table 3. For convenience, the day column was converted from text format to data format. Table 3. TXT files layout Description Broadcasting Station Code Type Initial Position Numeric (03) 1 Program Code Numeric (06) 24 Name of the Program Character (30) 30 Start of the Program Numeric (06) 160 End of the Program Numeric (06) 166 080000 6 11111110000 00000000000 03XX Start of the Program End of the Program Day of the Program Discarded Then, some contradictions about the time were noticed and immediately corrected so as the future analyses do not provide wrong results. This entire process was repeated for each of the 15 programming files, creating only one spread sheet with all the electronic programming guide of this 15-day period. The user behavior is composed of many spreadsheets called tuning spreadsheet which has much more information than the electronic programming guide. The tuning spreadsheets and the electronic program guide have codes which identify the Public broadcasting stations. There was the necessity of standardizing these codes because the identification number was registered in a different way in these files. In order to avoid data contradictions, a Broadcasting Station column was added in the electronic program guide and later the Public broadcasting stations codes were standardized due to the code conflicts among Bandeirantes, Record, Rede TV! and TV Cultura broadcasting stations. The day of the week and the duration of the program were also added. The electronic program guide is not concluded yet, there is still missing the genre and subgenre of each program. Therefore, the transmitted programs genre was searched in official sites of each broadcasting station and next was identified according to the ABNT NBR 15603-2:2007 Brazilian standard, attachment C, “Genre describer in the content describer” [28]. In order to make this identification easier, the filtering resource was used to classify the electronic program guide according to the name of the program. If the program was reprised within the 15day period, it would not be necessary to search again in the broadcasting station website. It is important to highlight that the electronic program guide spreadsheet totalized about 4,500 lines, what corresponds to 4,500 registers in a data bank and identified about 800 different programs. Picture 5 shows the program/category quantity relation found in the electronic program guide.
  6. 6. 3 Qauntity 200 Quantity 150 no people 2 no TVs 1 100 0 1 2 3 4 5 6 Houses 50 Picture 7. Characteristics of the monitored houses Category Picture 5. Program/category quantity relation The data format sent by IBOPE. can be seen in Picture 6 which shows users behavior from house 2. The spreadsheet starts at 00:00:00 and finishes at 05:59:00 a.m. and the channel code is recorded when the user watches the program. Despite the fact that there are 3 individuals and only 1 TV in house 2, IBOPE has collected the channels each person watched individually providing information about the behavior of each person in the house. Picture 7 shows the characteristics of the house. In order to work accordingly with the data, the tuning spreadsheet was also modified. Each person had to be separated with theirs respective channels, day, time house and TV. Date and time columns were also formulated according to the standard used in the Brazilian system. The same happened to all the spreadsheet contents, creating a relation which can be seen in Picture 8. The spreadsheets were converted in CSV files (Comma-separated values) to be inserted in MySQL data bank and also to be used in Weka. s Others Serires Infantile s News e Variaties Raffle, Telesales, Prize t Debate, Interview Prize TV Serires Educative n Sport Movie Show Humorous e Information Erotic s Soap Opera c Reality Show Miniseries 0 After, each CSV file was inserted in the data bank and the . unnecessary registers were discarded. Date and time columns were also converted in only one column according to the standard format (aaaa-mm-dd:hh:mm:ss). The next step was finding in the electronic program guide the programs correspondent to the viewings. In the proposed recommendation system the user behavior is monitored but not minute to minute, as it happens in IBOPE data, but when the user changes the channel. In order to attain this goal, data resulting from the mixture of the electronic program guide and the user behavior generating the viewing background, were treated again. Channel changes were identified, the program permanence time was calculated, the repeated registers and fields were deleted. Thus, the data was in compliance with the tests performed. 4. RESULTS The tests with Weka Apriori algorithm confirmed that this can be adopted in the system because it is adjustable to this propose necessities. From the rules created by Apriori, recommendations were simulated and it was possible to analyze if the user was watching the recommendation simulated by these rules. The following formula was used to calculate the accuracy: (1) in which a is the number of viewed recommendations, b is the number of performed recommendations and is the efficiency of the system. The results found in Pictures 9 and 10 are noticeable and make it clear that the tests were satisfactory during the period of evaluation. Picture 9 shows the quantity of recommendations the user viewed and requested in house 1 during 15days. The darkest line represents the viewed recommendations and the lightest line represents the requested recommendations. The average was of three recommendation viewings and two recommendation requests per day. Picture 10 shows the accuracy reaching an average of 77% during 15-day period. Picture 6. Tuning spreadsheet sample . It was possible to note other characteristics also related to the user in house 1 like the average of 30 minutes in front of the TV per day, 14 programs of different sub genres. Record and Globo as the most viewed station and Saturday as the day of the week in which the user spent more time in front of the TV.
  7. 7. 100 Accuracy 80 60 40 20 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Days Picture 8. Spreadsheet relation Picture 10. System Accuracy It was also possible to verify the size of the user background files. . The tests were iterative and cumulative, that is, data was collected on the first day of mining. On the second day, more data mined with the data from the first day was collected. It was verified that the data did not take more space proportionate to the number of mining days. Picture 11 shoes the size of the files created for the 15-day period in house 1. As future work, the program .classification and synopsis are intended to be included as parameter to discover user preferences. As for the synopsis, it could be possible to discover, for example, favorite movie actors and then recommend movies with these actors. Many other user preferences can be discovered through the program synopsis and our work intends to explore these options. 5. CONCLUSION The proposed recommendation system was designed considering current characteristics of portable devices and situations of using television in the cell phone. This model can be adjustable to other standards and also to new portable devices in the market. Recommendations / Solicitations Furthermore, there was a concerning of designing the system according with the Brazilian rules determined to portable devices, due particularly to current impracticability of developing the integrated system with a middleware to portable digital television so that in the future the implemented code can be portable with minimum modification and updating. 12 10 8 KyloBytes The reason of this work is the fact that digital television in cell phones is showing evidence of fast growth around the world. Furthermore, the possibility of watching TV anywhere and at any time in portable devices points that the personalization becomes important to solve some difficulties caused by overload of information in the EPG and also the time the users spend looking for programs they are interested in. 6 4 2 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Days Picture 11. Size of the viewing background files . 6. ACKNOWLEDGMENT We thank to IBOPE for providing real data of the electronic program guide and also the user behavior data from March 5 to March 19 2008. 6 4 7. REFERENCES 2 0 s r 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Days Picture 9. Viewed and Required Recommendations . 19 [1] Sistema Brasileiro de Televisão Digital. Available in: Access in August 16, 2009. [2] Fórum do Sistema Brasileiro de Televisão Digital. Available in: Access in August 17, 2009. [3] Electronic Programme Guide. Protocol for a TV Guide using electronic data transmission. ETSI standard ETS 300 707. Available in:
  8. 8. WKI_ID=3883. Access in August 18, 2009. [4] Televisão digital terrestre. Multiplexação e serviços de informação (SI) parte 3. Sintaxes e definições de informação estendida do SI. ABNT Norma Brasileira 15603-3. Available in: Access in August 16, 2009. [5] Service Information For Digital Broadcasting. ARIB standard STD-B10. Available in: Access in August 18, 2009. [6] Silva, Fábio Santos; Jucá, Paulyne Matthews. Personalização de Conteúdo Através de um Guia Eletrônico de Programação Personalizada para a TV Digital. WebMedia 2005: Simpósio Brasileiro de Sistemas Multimídia e Web, Workshop de Televisão Digital Interativa. 2005. [7] Bär, Arian et al. A Lightweight Mobile TV Recommender: Towards a One-Click-to-Watch Experience. In Proceedings 6th European Interactive TV Conference, p.142-147, Salzburg, Áustria, 03-04/07/2008. [8] Antenados assistem TV em qualquer lugar. Available in: Access in August 17, 2009. Gazeta Mercantil/Caderno D - Pág. 3. 19/05/2009. [9] Publicidade móvel e TV digital são negócios em ascensão. 30/07/2009. Available in: obj=noticia&mtd=detalhe&q=14942. Access in August 19, 2009. [10] Middleware Ginga. Available in: Access in August 20, 2009. [11] Comunidade do middleware Ginga no portal do software público. Available in: Access in August 20, 2009. [12] Ginga-NCL. Available in: Access in August 20, 2009. [13] Ginga-J. Available in: Access in August 20, 2009. [14] Nested Context Language, NCL. Available in: Access in August 20, 2009. [15] Hsu, S. H., Wen, M. H., Lin, H. C., Lee, C. C. and Lee, C. H.: AIMED, A personalized TV Recommendation System. In Proceedings of the Interactive TV: A Shared Experience, pages 166-174, Vol 4471, Springer Berlin / Heidelberg, 2007. [16] Zhiwen, Y., Xingshe, Z., Yanbin, H. and Jianhua, G. TV program recommendation for multiple viewers based on user profile merging. In Proceedings of the User Modeling and User-Adapted Interaction, pages 63-82. Publishing Springer Netherlands, 2006. [17] Zhang, H.; Zheng, S. Yuan J.: A personalized TV guide system compliant with MHP. In: Consumer Electronics, IEEE Transactions on, vol.51, no.2, pp. 731-737, 2005. [18] Digital Video Broadcasting, DVB. Available in: Access in August 18, 2009. [19] Open Mobile Alliance, OMA-BCAST. Available in: Access in August 19, 2009. [20] Solla, Alberto Gil et. al. ZapTV: Personalized UserGenerated Content for Handheld Devices in DVB-H Mobile Newtorks. In: Proceedings 6th European Interactive TV Conference, p.193-203, Salzburg, Áustria, 03-04/07/2008. [21] TV-Anytime. Available in: Access in August 22, 2009. [22] O'Reilly, Tim. What Is Web 2.0. Design Patterns and Business Models for the Next Generation of Software. 09/30/2005. Disponível em: Access in August 21, 2009. [23] Web Semântica. Available in: Access in August 24, 2009. [24] Uribe, Silvia, et al. Mobile TV Targeted Advertisement and Content Personalization. 16th International Workshop Conference on Systems, Signals and Image Processing, Chalkida, Greece, 18-19/06/2009. [25] Witten, I. H, Frank, Eibe. Data mining : practical machine learning tools and techniques. Cap. 4, seção 4.5, pg. 112. Elsevier. 2nd ed. 2005. [26] IBOPE. Available in: Access in August 28, 2009. [27] WEKA. Available in: Access in August 23, 2009. [28] Televisão digital terrestre. Multiplexação e serviços de informação (SI) parte 2. Estrutura de dados e definições da informação básica de SI. ABNT Norma Brasileira 15603-2. Available in: Access in August 30, 2009.