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
elaine_gatto@dc.ufscar.br
zorzo@dc.ufscar.br
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,
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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. 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. 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. 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. 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. 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. 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:
http://sbtvd.cpqd.com.br/. Access in August 16, 2009.
[2] Fórum do Sistema Brasileiro de Televisão Digital. Available
in: http://www.forumsbtvd.org.br/. 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. http://webapp.etsi.org/workprogram/Report_WorkItem.asp?
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: http://www.forumsbtvd.org.br/materias.asp?id=112.
Access in August 16, 2009.
[5] Service Information For Digital Broadcasting. ARIB
standard STD-B10. Available in:
http://www.dibeg.org/techp/techp.htm. 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:
http://www.forumsbtvd.org.br/materias.asp?id=131. 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: http://sbtvd.cpqd.com.br/?
obj=noticia&mtd=detalhe&q=14942. Access in August 19,
2009.
[10] Middleware Ginga. Available in: http://www.ginga.org.br/.
Access in August 20, 2009.
[11] Comunidade do middleware Ginga no portal do software
público. Available in:
http://www.softwarepublico.gov.br/dotlrn/clus/ginga. Access
in August 20, 2009.
[12] Ginga-NCL. Available in: http://www.gingancl.org.br/.
Access in August 20, 2009.
[13] Ginga-J. Available in: http://www.openginga.org/index.html.
Access in August 20, 2009.
[14] Nested Context Language, NCL. Available in:
http://www.ncl.org.br/. 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:
http://www.dvb.org. Access in August 18, 2009.
[19] Open Mobile Alliance, OMA-BCAST. Available in:
http://www.openmobilealliance.org/. 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: http://www.tv-anytime.org/.
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:
http://oreilly.com/web2/archive/what-is-web-20.html. Access
in August 21, 2009.
[23] Web Semântica. Available in:
http://www.w3.org/2001/sw/SW-FAQ#What1. 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: http://www.ibope.com.br. Access in
August 28, 2009.
[27] WEKA. Available in: http://www.cs.waikato.ac.nz/ml/weka/.
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:
http://www.forumsbtvd.org.br/materiais.asp?id=112. Access
in August 30, 2009.