BIPoDi TVR: Brazilian Interactive Portable Digital TV                    Recommendation System                      Elaine...
In Brazil, the quantity of cell phones is much bigger than the          distances between the topics are calculated. For e...
responsible for formatting the data providing a safe and adequate      tests and the implementation were performed in Ging...
The BIPoDi TVR Trigger is responsible for starting and finishingthe data processing of the system. The BIPoDi TVR Capture ...
Table 4. Identifying the fields in TXT files                                                                           Col...
200                                                                                                                  3    ...
100                                                                                                                       ...        [16] Zhiwen, Y., Xingshe, Z., Yanbin, H. and Jianhua, G. TV...
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Sigap bi po-ditvr brazilian interactive portable digital tv recommendation system


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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.

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Sigap bi po-ditvr 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 Universidade Federal de São Carlos Rodovia Washington Luís, Km 235 Rodovia Washington Luís, Km 235 Caixa Postal 676, CEP 13565-905 Caixa Postal 676, CEP 13565-905 Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil zorzo@dc.ufscar.brABSTRACT some kind of interactivity for the portable digital television hasUsing the Brazilian digital television system, the possibility of been already offered in some countries which have this service,offering new services and programs, and consequently more for example, voting in programs, shopping advertisement,available content, will make it difficult for the users to select their electronic programming guide, etc.favorite programs. The Recommendation Systems become a tool The electronic programming guide [3, 4, 5] helps the user to findto solve these difficulties and they are able to improve the TV program he wants to watch. However, the increase ofinteractivity between the user and the digital television filtering content in electronic programming guide is unavoidable with theinformation filtering and personalizing the content offer. This inclusion of new channels and, due to the great quantity ofpaper describes a recommendation system for Brazilian information; the user starts to find difficulties in choosinginteractive portable digital television focused on the cell phone programs, resulting in waste of time. The electronic programmingwhich makes this functionality possible and creates TV program guide, overloaded with information, does not meet the userrecommendation according to user TV programs preferences necessity, as it does not take their preferences in account, and thewhen using television in the cell phone. lists presentation on the screen becomes boring because they are long.Categories and Subject Descriptors For the portable TV users, this situation is even more aggravating.H.3.3 [Information Storage and Retrieval]: Information Search The presentation of long programming lists on a reduced screenand Retrieval – Selection Process , Information Filtering; will bring even more difficulties. So, the interactive portableH.5.1 [Information Interfaces and Presentation]: Multimedia digital television users focus on the current lack of the deviceInformation Systems. resources and do not want to waste their time selecting programs. Different from using digital television in houses where it isGeneral Terms common to change channels frequently and navigate theAlgorithms, Desgin. electronic programming guide, interactive portable digital television takes considerable time and energy. [6, 7]KeywordsMiddleware Ginga, Mobile TV, Multimedia, Personalization, Table 1. Comparison between permanent and portable digitalProfiling, Recommendation System. television in Brazil1. INTRODUCTION Permanent PortableNew services, products, contents, channels and business models Set-top-box PDAs cell phones, Mini-TVs,have been created with the digital television. The Brazilian digital TV sets with built-in Smartphone’s, Blackberries,television system [1, 2] allows permanent and portable reception, converter Receptors for automobileshigh audio and video quality and interactivity, creating differentcontents for permanent and portable interactive digital television Many users One userusers. The interactive portable digital television shares in only one Screen bigger than 30 inches Screen bigger than 10 inchesdevice, internet, TV, cell phone, and the TV signs for thesedevices are already available in many Brazilian cities. Nowadays, Permanent place Anywhere Longer viewing time Shorter viewing time 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 Return channel from the cell not made or distributed for profit or commercial advantage and that No Return Channel defined net 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, Reference implementation of Reference implementation of requires prior specific permission and/or a fee. the available middleware the non-available middleware SAC’10, March 22-26, 2010, Sierre, Switzerland. Copyright 2010 ACM 978-1-60558-638-0/10/03…$10.00.
  2. 2. In Brazil, the quantity of cell phones is much bigger than the distances between the topics are calculated. For each entry, anquantity of TV sets, what can quickly stimulate the use of digital index is calculated and a list of programs organized by this indextelevision in this kind of device when theses cell phones with is TV become more accessible to the population. [8, 9] The ZapTV [20] developed for DVB-H standard allows the userThe main advantage of the portable digital television is that the to create his own content, offering aggregated value services asuser can use it in any place and at any time. On the other hand, the multimodal access (Web and Cell phones), return channel, videoadvantage of permanent digital television is watching the note, personalized sharing and distribution of content. Besides theprograms at home for a longer time. Table 1 shows a comparison technology provided by DVB-H, ZapTV comprehends otherbetween the permanent and portable digital television in Brazil. technologies as TV-Anytime [21], Technologies emerging fromThe users of these devices need private attention due to the Web 2.0 [22] and involved in the Semantic Web [23].current characteristics of this environment like processing power,storage capacity and battery. The main functionalities of ZapTV include a social net, personalized content broadcasting (implicit or explicitIn order to enjoy all the potential provided by the interactive recommendation), thematic channels diffusion planning (age-portable digital television, a software is necessary to link the group, genre or specific theme), client application andhardware, the operational system and the digital television transmission of the electronic programming guide.interactive applications. Such software is the middleware calledGinga in Brazil [10, 11]. Ginga middleware allows the ZapTV seeks to improve the recommendation using an intelligentconstruction of declarative and procedural applications using personalization mechanism which matches information filteringGinga-NCL (Nested Context Language) [12] and Ginga-J (Java) with semantic logic processes and it was based on the principles[13] respectively. of participation and sharing between Web 2.0 users, so that the creation, sharing, classification and note of content make theThe proposed model in this work used a Ginga-NCL middleware search for content easier.reference implementation. NCL [14] is a declarative languageused to authorize hypermedia documents and it was developed The main purpose of the system is replacing the ordinary contentbased on a conceptual model which focuses on representing and (Public Broadcasting Station) by a personalized and adjusted onetreating hypermedia documents. NCL is Ginga-NCL official in order to provide more attractive content for the users. Thelanguage and it can be used in portable devices. system architecture allows diffusing content both by broadcast, like DVB-H, and by video streaming.Finally, the main goal of this work is to develop arecommendation system for Brazilian interactive portable digital There is a server which locates the television flow and the datatelevision in order to recommend TV programs according to the service; and a content personalized server which is responsible foruser profile. attributing and managing personal content according to the user preferences and viewing background as well as indicating when aThis paper is divided in: section 1 presenting the context of the change from the ordinary content to a personalized content mustwork, section 2 presenting some correlated works, section 3 be performed.presenting the recommendation system for Brazilian interactiveportable digital television, as well as its characteristics, The user section consists of portable devices which can performarchitecture and implementation, section 4 presenting the results the client application and send back to the server the necessaryand section 5 the conclusion. 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 device2. CORRELATED WORKS and there is also a module to store the user data collection andThere are many recommendation systems for set-top-boxes personalized content received from the server.allowing personalization services. More information about thesesystems can be found in [15, 16, 17]. Developing recommendation There is a module called control which is responsible forsystems for cell phones with television is a current area of performing the player when the user starts the applicative,research. Three works which applies recommendation techniques monitoring, capturing and preparing the user interactions to befor interactive portable digital television are presented bellow. sent to the server among other tasks. The last module on the client side is responsible for receiving the personalized content andIn [7] a recommendation system for the DVB-H (Digital Video sending the captured data.Broadcast – Handheld) standard [18] was developed according toOMA-BCAST (Open Mobile Alliance-Mobile Broadcast Services The Decissor module, on the server side controls the user profilesEnabler Suite) [19]. The authors have identified some in the data bank module, updates the user profile whenever itrequirements for the recommendation systems dedicated to this receives information from the user about the behavior and selectsenvironment as scalability, response latency, flexibility for current advertisements which have to be sent to the users according tostandards of transmission, user privacy protection, among others. their profiles. The Web Server lodges the web services to manageThe recommendation system is in the category of systems with the system and the contents; and advertisements companies andfiltering based on content using text mining. content providers can add, delete and modify contents, programs and users.It uses a simple interface with the user and accepts naturallanguage as text entry as well as four values reflecting the user There is also a module to control the data flow between the serverpreferences for comedy, action, horror and eroticism. The and the user and other module to the data bank which store therecommendation in this system occurs as follows: first, the texts profiles, the data collected from the user behavior and the contentsare extracted, next, the emotion in the text is analyzed and the sent by providers. The last module on the server side is
  3. 3. responsible for formatting the data providing a safe and adequate tests and the implementation were performed in Ginga-NCLcommunication among the modules. middleware for set-top-box because the implementation for this middleware portable device is not available at the moment.Concluding, the system requires login/password and when theuser accesses the application for the first time, he fills in a formwith his preferences in order to generate his profile. After loggingin, the user starts watching television either by streaming or bybroadcast.Both works aforementioned provide solutions to thepersonalization and the information overload in digital televisionin portable devices.In [7] the recommendation system mechanism applies twotechniques: the text mining and filtering based in content besidesrequiring some data from the; while in [20], the mechanism ismore sophisticated, using hybrid information filtering, semanticlogic and explicit and implicit user identification. The login isnecessary in all of them and the differentials of [24] are thepersonalized advertisement and the reception of content either bystreaming or by broadcast.The work proposed in this article uses a data mining algorithmand implicit collection of the user behavior, which does notrequire login/password from the user, and was particularly Picture 1. Context of the system usedeveloped for the Brazilian digital television system. However, itsmodel can be applied in other standards. The processing starts when the user turns on the TV in his cell . phone. The user viewing background data collected until thatThe recommendation systems from previous work are out of theportable device, and this is the most noticeable difference of moment are mined in order to find the user profile.The datamodel proposed in this work. Both systems include, inside resulting from the mining are formatted and the user profile isexisting digital television architecture, its own architecture, like stored in a data bank, together with date and time of generation.content servers and electronic program guide servers. Once the user profile is updated, he can look in the electronicIn this work, the recommendation system is in the portable device program guide for compatible TV programs with transmissionand the inclusion of servers in Brazilian interactive portable time close to the current time, generating a list with thesedigital television is not necessary for providing recommendation programs.and, therefore, there is no need of remote communication, The list is cleaned and formatted and only the data related to date,avoiding the user to pay by data traffic in the net to receive the time, duration and broadcast station remains generating a new listrecommendation or send data, protecting the user data privacy. of programs. The list with the programs includes the recommendations which are also stored in a data base with the3. BIPODI TVR date and time of generation.The system proposed in this work aims at making easier the The recommendations are presented to the user and those whichinteractive portable digital television user routine by interacting are required are stored with the viewing background. All thethrough a simple interface which allows the user to watch his programs the user watched during the period the TV is turned onfavorite content without spending too much time to find it. in the cell phone are stored in the viewing background.BIPoDi TVR (Brazlian Interactive Portable Digital TV All the programs the user watched during the period the TV isRecommender) was projected in order to be executed locally in turned on in the cell phone are stored in the data base whichthe cell phone with the digital television functionality. It is also contains the viewing background. This process is repeatednecessary that the device has Ginga-NCL middleware. Picture 1 whenever the user turns the TV on.shows the context to use BIPoDi TVR. The fixed and mobilereceptors receive audio, video and data and the middleware is 3.1 Implementationresponsible for separating them. Ginga middleware has a layer for the resident applicationsThe device must be able to receive the digital television responsible for exhibition, other layer for the common core,transmission with the help of an internal or external antenna responsible for offering many services, and a last layer pertinentcompatible with the standard transmission adopted in Brazil. The to the pile protocols. BIPoDi TVR was implemented as anuser interacts with the television in the cell phone and all the element in Ginga architecture, in the common core layer (Gingachannels viewed during the period of use are stored. Common Core), as illustrated in Picture 2.The initial propose of BIPoDi TVR considers using the categories BIPoDi TVR is divided in many modules and it was carefullyand the TV programs start time. As soon as the user turns on the thought, designed and modeled particularly to portable devices,TV in the cell phone, TV programs of his preference with time considering its current characteristics in order to meet theclose to current time are recommended. requirements of this environment and to agree with the BrazilianBIPoDi TVR was developed using Ginga-NCL middleware. The rules for portable digital television.
  4. 4. The BIPoDi TVR Trigger is responsible for starting and finishingthe data processing of the system. The BIPoDi TVR Capture isresponsible for capturing and storing all the programs watchedduring the period the TV is turned on in the cell phone, as well asthe 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. 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. Picture 2. Recommendation system in Ginga Association discoveries should point common and not so common middleware architecture associations. Apriori algorithm is frequently used for mining association rules and can work with a high number of attributes creating manyThe BIPoDi TVR Mining . is responsible for storing the user combinations among them and successively searching all dataprofile. This module should also find, in the electronic program base, keeping an excellent performance relating the processingguide the programs which can be recommended to the user time.according to the profile generating results with completeinformation. The BIPoDi TVR Filter is responsible for filtering The algorithm tries to find all the relevant association rules amongthe relevant information resulting from the Mining module, the items which have the X (prior) ==> Y (consequent) shape. Ifformatting them and creating a list of recommendation. x% of the transaction containing X also contains Y, so x% represents the confidence factor (confidence force of the rule).The BIPoDi TVR Presentation is responsible for presenting The support factor corresponds to x% of times that X and Y occurrecommendation as well as managing the time the simultaneously on the total of registers (frequency). [25]recommendation will be on the screen. The last module, BIPoDiTVR Data Manager, is responsible for deleting the data as soon as In order to prove that this algorithm meets the necessarythey became old. requirements of this work, the tests were performed using data from house 1 and Apriori algorithm of Weka software. Table 2BIPoDi TVR architecture has also data bases (files) to store the shows a sample of the rules created by the software. Rule 1user viewing background, the electronic program guide, the user indicates that the Variety/Others describer had 21 occurrences inprofile and the recommendations. Picture 3 shows the Record broadcasting station in house 1.recommendation system architecture.3.2 Mining Algorithm Table 2. Sample of rules created by WekaThe BIPoDi TVR Mining module uses a mining algorithm.Among the several existent data mining methods and considering No Rulesthe domain specificities of this application, it was possible to domicilio=1 nomeEmissora=Recordverify that the bottom-up method in which the exploring process 1 descSubGenero=Outros 21 ==>tries to discover something that is not known yet by extracting descGenero=Variedade 21 conf:(1)only the data standards, as well as the indirect or non supervised descGenero=Jornalismo 9 ==> domicilio=3 29 2knowledge search method and the association tasks are the most conf:(1)
  5. 5. Table 4. Identifying the fields in TXT files Column Content Identification Broadcasting 005 005100PNRE Station code 1 XXXXX 100PNREX Discarded XXXX 002645 Program Code 002645RELI 2 RELIGIOS Name of the GIOSO MAT O MAT Program 3 000000 Discarded 4 0000 Discarded Start of the 060000 Program Picture 4. Sample of the TXT files initial layout 06000008000 End of the 5 080000 0DIA_05 Program DIA_0 Day of the3.3 Tests . 5 ProgramIn order to test the proposed and implemented system, particularly 11111110000the mining algorithm, it is necessary to have the user viewing data 6 00000000000 Discardedand also the electronic program guide. This data was provided by 03XXIBOPE [26] and was treated through an almost entirely manualprocess in order to fit the standard format used in Brazilian digitaltelevision system and also to be used in Weka mining data Then, some contradictions about the time were noticed andsoftware [27] for the tests. immediately corrected so as the future analyses do not provide wrong results. This entire process was repeated for each of the 15The data corresponds to 15 days of programming and monitoring programming files, creating only one spread sheet with all theof 6 Brazilian houses. The electronic program guide is composed electronic programming guide of this 15-day period.of 15 TXT files called programming files, one for each day (fromMarch 3 2008 to March 19 2008) with 10 public broadcasting The user behavior is composed of many spreadsheets calledstations starting at 00:00:00 and finishing at 05:59:00 a.m. Picture tuning spreadsheet which has much more information than the4 shows a sample of initial layout of these files and Table 3 shows electronic programming guide. The tuning spreadsheets and thehow this layout was organized. electronic program guide have codes which identify the Public broadcasting stations. There was the necessity of standardizingWith the first line from Picture 4 as an example, it is possible to these codes because the identification number was registered in aidentify field according to Table 4. After understanding the files different way in these files.composing the electronic program guide, the data was copiedfrom the programming files to a BrOffice spreadsheet with paste In order to avoid data contradictions, a Broadcasting Stationspecial resource. This resource allowed the data to be exported column was added in the electronic program guide and later theexactly as it was built in the layout, separating the fields in Public broadcasting stations codes were standardized due to thecolumns. code conflicts among Bandeirantes, Record, Rede TV! and TV Cultura broadcasting stations.After exporting, the unnecessary data was discarded. At themoment of exporting, the numeric data lost its format and then it The day of the week and the duration of the program were alsowas reformatted according to Table 3. For convenience, the day added. The electronic program guide is not concluded yet, there iscolumn was converted from text format to data format. 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 Table 3. TXT files layout ABNT NBR 15603-2:2007 Brazilian standard, attachment C, “Genre describer in the content describer” [28]. Description Type Initial Position Broadcasting Station In order to make this identification easier, the filtering resource Numeric (03) 1 was used to classify the electronic program guide according to the Code name of the program. If the program was reprised within the 15- Program Code Numeric (06) 24 day period, it would not be necessary to search again in the Name of the Program Character (30) 30 broadcasting station website. It is important to highlight that the electronic program guide Start of the Program Numeric (06) 160 spreadsheet totalized about 4,500 lines, what corresponds to 4,500 End of the Program Numeric (06) 166 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. 200 3 no people Qauntity 2 no TVs 150Quantity 1 100 0 1 2 3 4 5 6 Houses 50 Picture 7. Characteristics of the monitored houses After, each CSV file was inserted in the data bank and the 0 . unnecessary registers were discarded. Date and time columns Soap Opera t n c e e s Movie s s Humorous Miniseries TV Serires News Sport Information Erotic Infantile Educative Debate, Interview Others Serires Variaties Raffle, Telesales, Prize Show Reality Show Prize 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 Category minute to minute, as it happens in IBOPE data, but when the user changes the channel. Picture 5. Program/category quantity relation 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 wereThe data format sent by IBOPE. can be seen in Picture 6 which identified, the program permanence time was calculated, theshows users behavior from house 2. The spreadsheet starts at repeated registers and fields were deleted. Thus, the data was in00:00:00 and finishes at 05:59:00 a.m. and the channel code is compliance with the tests performed.recorded when the user watches the program.Despite the fact that there are 3 individuals and only 1 TV in 4. RESULTShouse 2, IBOPE has collected the channels each person watched The tests with Weka Apriori algorithm confirmed that this can beindividually providing information about the behavior of each adopted in the system because it is adjustable to this proposeperson in the house. Picture 7 shows the characteristics of the necessities. From the rules created by Apriori, recommendationshouse. were simulated and it was possible to analyze if the user wasIn order to work accordingly with the data, the tuning spreadsheet watching the recommendation simulated by these rules. Thewas also modified. Each person had to be separated with theirs following formula was used to calculate the accuracy:respective channels, day, time house and TV. Date and timecolumns were also formulated according to the standard used inthe Brazilian system. The same happened to all the spreadsheet (1)contents, creating a relation which can be seen in Picture 8.The spreadsheets were converted in CSV files (Comma-separatedvalues) to be inserted in MySQL data bank and also to be used in in which a is the number of viewed recommendations, b is theWeka. 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. 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 Picture 6. Tuning spreadsheet sample the user spent more time in front of the TV. .
  7. 7. 100 80 Accuracy 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 AccuracyIt was also possible to verify the size of the user background files. .The tests were iterative and cumulative, that is, data was collectedon the first day of mining. On the second day, more data mined As future work, the program .classification and synopsis arewith the data from the first day was collected. It was verified that intended to be included as parameter to discover user preferences.the data did not take more space proportionate to the number of As for the synopsis, it could be possible to discover, for example,mining days. Picture 11 shoes the size of the files created for the favorite movie actors and then recommend movies with these15-day period in house 1. actors. Many other user preferences can be discovered through the program synopsis and our work intends to explore these options.5. CONCLUSION 12The reason of this work is the fact that digital television in cellphones is showing evidence of fast growth around the world. 10Furthermore, the possibility of watching TV anywhere and at anytime in portable devices points that the personalization becomes 8important to solve some difficulties caused by overload ofinformation in the EPG and also the time the users spend looking KyloBytes 6for programs they are interested in.The proposed recommendation system was designed considering 4current characteristics of portable devices and situations of usingtelevision in the cell phone. This model can be adjustable to other 2standards and also to new portable devices in the market.Furthermore, there was a concerning of designing the system 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19according with the Brazilian rules determined to portable devices,due particularly to current impracticability of developing the Daysintegrated system with a middleware to portable digital televisionso that in the future the implemented code can be portable with Picture 11. Size of the viewing background filesminimum modification and updating. . 6. ACKNOWLEDGMENT Recommendations / Solicitations 6 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. 4 7. REFERENCES 2 s [1] Sistema Brasileiro de Televisão Digital. Available in: r 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Access in August 16, 2009. Days [2] Fórum do Sistema Brasileiro de Televisão Digital. Available in: Access in August 17, 2009. Picture 9. Viewed and Required Recommendations [3] Electronic Programme Guide. Protocol for a TV Guide using electronic data transmission. ETSI standard ETS 300 707. Available in: .
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