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.

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
elaine_gatto@dc.ufscar.br zorzo@dc.ufscar.br
ABSTRACT some kind of interactivity for the portable digital television has
Using 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 find
to solve these difficulties and they are able to improve the TV program he wants to watch. However, the increase of
interactivity between the user and the digital television filtering content in electronic programming guide is unavoidable with the
information filtering and personalizing the content offer. This inclusion of new channels and, due to the great quantity of
paper describes a recommendation system for Brazilian information; the user starts to find difficulties in choosing
interactive portable digital television focused on the cell phone programs, resulting in waste of time. The electronic programming
which makes this functionality possible and creates TV program guide, overloaded with information, does not meet the user
recommendation according to user TV programs preferences necessity, as it does not take their preferences in account, and the
when 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 screen
and Retrieval – Selection Process , Information Filtering; will bring even more difficulties. So, the interactive portable
H.5.1 [Information Interfaces and Presentation]: Multimedia digital television users focus on the current lack of the device
Information Systems. resources and do not want to waste their time selecting programs.
Different from using digital television in houses where it is
General Terms common to change channels frequently and navigate the
Algorithms, Desgin. electronic programming guide, interactive portable digital
television takes considerable time and energy. [6, 7]
Keywords
Middleware Ginga, Mobile TV, Multimedia, Personalization,
Table 1. Comparison between permanent and portable digital
Profiling, Recommendation System.
television in Brazil
1. INTRODUCTION Permanent Portable
New 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 automobiles
high audio and video quality and interactivity, creating different
contents for permanent and portable interactive digital television Many users One user
users. The interactive portable digital television shares in only one
Screen bigger than 30 inches Screen bigger than 10 inches
device, internet, TV, cell phone, and the TV signs for these
devices 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. In Brazil, the quantity of cell phones is much bigger than the distances between the topics are calculated. For each entry, an
quantity of TV sets, what can quickly stimulate the use of digital index is calculated and a list of programs organized by this index
television in this kind of device when theses cell phones with is recaptured.
digital TV become more accessible to the population. [8, 9]
The ZapTV [20] developed for DVB-H standard allows the user
The main advantage of the portable digital television is that the to create his own content, offering aggregated value services as
user can use it in any place and at any time. On the other hand, the multimodal access (Web and Cell phones), return channel, video
advantage of permanent digital television is watching the note, personalized sharing and distribution of content. Besides the
programs at home for a longer time. Table 1 shows a comparison technology provided by DVB-H, ZapTV comprehends other
between the permanent and portable digital television in Brazil. technologies as TV-Anytime [21], Technologies emerging from
The 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 explicit
In 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 and
hardware, the operational system and the digital television transmission of the electronic programming guide.
interactive applications. Such software is the middleware called
Ginga in Brazil [10, 11]. Ginga middleware allows the ZapTV seeks to improve the recommendation using an intelligent
construction of declarative and procedural applications using personalization mechanism which matches information filtering
Ginga-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 the
The proposed model in this work used a Ginga-NCL middleware search for content easier.
reference implementation. NCL [14] is a declarative language
used to authorize hypermedia documents and it was developed The main purpose of the system is replacing the ordinary content
based on a conceptual model which focuses on representing and (Public Broadcasting Station) by a personalized and adjusted one
treating hypermedia documents. NCL is Ginga-NCL official in order to provide more attractive content for the users. The
language 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 a
recommendation system for Brazilian interactive portable digital There is a server which locates the television flow and the data
television in order to recommend TV programs according to the service; and a content personalized server which is responsible for
user profile. attributing and managing personal content according to the user
preferences and viewing background as well as indicating when a
This paper is divided in: section 1 presenting the context of the change from the ordinary content to a personalized content must
work, section 2 presenting some correlated works, section 3 be performed.
presenting the recommendation system for Brazilian interactive
portable digital television, as well as its characteristics, The user section consists of portable devices which can perform
architecture and implementation, section 4 presenting the results the client application and send back to the server the necessary
and 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 device
2. CORRELATED WORKS and there is also a module to store the user data collection and
There are many recommendation systems for set-top-boxes personalized content received from the server.
allowing personalization services. More information about these
systems can be found in [15, 16, 17]. Developing recommendation There is a module called control which is responsible for
systems 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 be
for 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 and
In [7] a recommendation system for the DVB-H (Digital Video sending the captured data.
Broadcast – Handheld) standard [18] was developed according to
OMA-BCAST (Open Mobile Alliance-Mobile Broadcast Services The Decissor module, on the server side controls the user profiles
Enabler Suite) [19]. The authors have identified some in the data bank module, updates the user profile whenever it
requirements for the recommendation systems dedicated to this receives information from the user about the behavior and selects
environment as scalability, response latency, flexibility for current advertisements which have to be sent to the users according to
standards of transmission, user privacy protection, among others. their profiles. The Web Server lodges the web services to manage
The recommendation system is in the category of systems with the system and the contents; and advertisements companies and
filtering 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 natural
language as text entry as well as four values reflecting the user There is also a module to control the data flow between the server
preferences for comedy, action, horror and eroticism. The and the user and other module to the data bank which store the
recommendation in this system occurs as follows: first, the texts profiles, the data collected from the user behavior and the contents
are extracted, next, the emotion in the text is analyzed and the sent by providers. The last module on the server side is

3. responsible for formatting the data providing a safe and adequate tests and the implementation were performed in Ginga-NCL
communication 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 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.
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 Picture 1. Context of the system use
developed for the Brazilian digital television system. However, its
model 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 that
The recommendation systems from previous work are out of the
portable device, and this is the most noticeable difference of moment are mined in order to find the user profile.The data
model proposed in this work. Both systems include, inside resulting from the mining are formatted and the user profile is
existing 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 electronic
In this work, the recommendation system is in the portable device program guide for compatible TV programs with transmission
and the inclusion of servers in Brazilian interactive portable time close to the current time, generating a list with these
digital 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 list
recommendation 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 the
3. 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 which
interactive portable digital television user routine by interacting are required are stored with the viewing background. All the
through a simple interface which allows the user to watch his programs the user watched during the period the TV is turned on
favorite 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 is
Recommender) was projected in order to be executed locally in turned on in the cell phone are stored in the data base which
the cell phone with the digital television functionality. It is also contains the viewing background. This process is repeated
necessary 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 mobile
receptors receive audio, video and data and the middleware is 3.1 Implementation
responsible for separating them. Ginga middleware has a layer for the resident applications
The 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 pertinent
compatible with the standard transmission adopted in Brazil. The to the pile protocols. BIPoDi TVR was implemented as an
user interacts with the television in the cell phone and all the element in Ginga architecture, in the common core layer (Ginga
channels 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 carefully
and 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 the
close to current time are recommended. requirements of this environment and to agree with the Brazilian
BIPoDi TVR was developed using Ginga-NCL middleware. The 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.
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 many
The BIPoDi TVR Mining . is responsible for storing the user combinations among them and successively searching all data
profile. This module should also find, in the electronic program base, keeping an excellent performance relating the processing
guide the programs which can be recommended to the user time.
according to the profile generating results with complete
information. The BIPoDi TVR Filter is responsible for filtering The algorithm tries to find all the relevant association rules among
the relevant information resulting from the Mining module, the items which have the X (prior) ==> Y (consequent) shape. If
formatting 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 occur
recommendation as well as managing the time the simultaneously on the total of registers (frequency). [25]
recommendation will be on the screen. The last module, BIPoDi
TVR Data Manager, is responsible for deleting the data as soon as In order to prove that this algorithm meets the necessary
they became old. requirements of this work, the tests were performed using data
from house 1 and Apriori algorithm of Weka software. Table 2
BIPoDi TVR architecture has also data bases (files) to store the shows a sample of the rules created by the software. Rule 1
user viewing background, the electronic program guide, the user indicates that the Variety/Others describer had 21 occurrences in
profile 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 Weka
The BIPoDi TVR Mining module uses a mining algorithm.
Among the several existent data mining methods and considering No Rules
the domain specificities of this application, it was possible to domicilio=1 nomeEmissora=Record
verify 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
2
knowledge search method and the association tasks are the most conf:(1)

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 the
3.3 Tests .
5 Program
In order to test the proposed and implemented system, particularly
11111110000
the mining algorithm, it is necessary to have the user viewing data
6 00000000000 Discarded
and also the electronic program guide. This data was provided by
03XX
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 Then, some contradictions about the time were noticed and
software [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 15
The data corresponds to 15 days of programming and monitoring
programming files, creating only one spread sheet with all the
of 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 (from
March 3 2008 to March 19 2008) with 10 public broadcasting The user behavior is composed of many spreadsheets called
stations starting at 00:00:00 and finishing at 05:59:00 a.m. Picture tuning spreadsheet which has much more information than the
4 shows a sample of initial layout of these files and Table 3 shows electronic programming guide. The tuning spreadsheets and the
how this layout was organized. electronic program guide have codes which identify the Public
broadcasting stations. There was the necessity of standardizing
With the first line from Picture 4 as an example, it is possible to
these codes because the identification number was registered in a
identify field according to Table 4. After understanding the files
different way in these files.
composing the electronic program guide, the data was copied
from the programming files to a BrOffice spreadsheet with paste In order to avoid data contradictions, a Broadcasting Station
special resource. This resource allowed the data to be exported column was added in the electronic program guide and later the
exactly as it was built in the layout, separating the fields in Public broadcasting stations codes were standardized due to the
columns. code conflicts among Bandeirantes, Record, Rede TV! and TV
Cultura broadcasting stations.
After exporting, the unnecessary data was discarded. At the
moment of exporting, the numeric data lost its format and then it The day of the week and the duration of the program were also
was reformatted according to Table 3. For convenience, the day added. The electronic program guide is not concluded yet, there is
column 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. 200 3 no people
Qauntity
2 no TVs
150
Quantity
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 were
The data format sent by IBOPE. can be seen in Picture 6 which
identified, the program permanence time was calculated, the
shows users behavior from house 2. The spreadsheet starts at
repeated registers and fields were deleted. Thus, the data was in
00:00:00 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. RESULTS
house 2, IBOPE has collected the channels each person watched
The tests with Weka Apriori algorithm confirmed that this can be
individually providing information about the behavior of each
adopted in the system because it is adjustable to this propose
person in the house. Picture 7 shows the characteristics of the necessities. From the rules created by Apriori, recommendations
house. were simulated and it was possible to analyze if the user was
In order to work accordingly with the data, the tuning spreadsheet watching the recommendation simulated by these rules. The
was 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 time
columns were also formulated according to the standard used in
the 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-separated
values) to be inserted in MySQL data bank and also to be used in in which a is the number of viewed recommendations, b is the
Weka. 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. 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 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 As future work, the program .classification and synopsis are
with 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 these
15-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 12
The reason of this work is the fact that digital television in cell
phones is showing evidence of fast growth around the world. 10
Furthermore, the possibility of watching TV anywhere and at any
time in portable devices points that the personalization becomes 8
important to solve some difficulties caused by overload of
information in the EPG and also the time the users spend looking
KyloBytes
6
for programs they are interested in.
The proposed recommendation system was designed considering 4
current characteristics of portable devices and situations of using
television in the cell phone. This model can be adjustable to other
2
standards 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 19
according with the Brazilian rules determined to portable devices,
due particularly to current impracticability of developing the Days
integrated system with a middleware to portable digital television
so that in the future the implemented code can be portable with Picture 11. Size of the viewing background files
minimum 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
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