Currently in Brazil, a fundamental change is taking place in TV: the migration from analogue to digital TV system. This change has two main implications: an increase in transmission capacity for new channels with the same bandwidth and the ability to send applications with multiplexed audio-visual content. Brazilian government aims to exploit the transmission capacity for new channels offering programming created to distance learning and thereby promoting social inclusion in the vast majority of the population. This information overload demands mechanisms to help students to browse and select what education programs are best suited to their current level. Personalized recommendation systems emerge as a solution to this problem, providing the viewer with educational programs relevant to his profile. In this paper we present a personalized recommendation system, the Recommender consistent with the reference implementation of the Brazilian digital TV system. Finally, we present the results obtained after using the proposed system.

1. Session S1E
Recommender: Helping Viewers in their Choice for
Educational Programs in Digital TV Context
Paulo Muniz de Ávila, Elaine Cecília Gatto, Sergio Donizetti Zorzo
pauloavila @pucpocos.com.br,elaine_gatto@dc.ufscar.br,zorzo@dc.ufscar.br
Abstract - Currently in Brazil, a fundamental change is favorite program. In face of this situation, personalized
taking place in TV: the migration from analogue to recommendation systems are necessary.
digital TV system. This change has two main Different from EPG functions which allow basic search,
implications: an increase in transmission capacity for a personalized TV system can create a profile for each TV
new channels with the same bandwidth and the ability to viewer and recommend programs that best match this
send applications with multiplexed audio-visual content. profile, avoiding the search in many EPG options to find the
Brazilian government aims to exploit the transmission favorite program. Elementary and secondary education
capacity for new channels offering programming created schools and universities generally seek to explore this new
to distance learning and thereby promoting social model offering personalized content to their students. In this
inclusion in the vast majority of the population. This context, a recommendation system is able to analyze the
information overload demands mechanisms to help profile of a group of students, suggesting the educational
students to browse and select what education programs content that best suits the needs of the group.
are best suited to their current level. Personalized To make the benefits (new channels, interactive
recommendation systems emerge as a solution to this applications) offered by the digital system possible, the TV
problem, providing the viewer with educational viewers with analogical system need new equipment called
programs relevant to his profile. In this paper we present set-top box (STB). STB is a device which works connected
a personalized recommendation system, the to the TV and converts the digital sign received from the
Recommender consistent with the reference provider to audio/video that the analogical TV can exhibit.
implementation of the Brazilian digital TV system. To have the advantages offered by the digital TV, the STB
Finally, we present the results obtained after using the needs a software layer which connects the hardware to the
proposed system. interactive applications called middleware. The DTV
Brazilian System middleware is Ginga [2,3]. It allows
Key-words - Personalization, Multimedia, Recommendation declarative and procedural applications through its
System, Digital TV, Middleware Ginga. components Ginga-NCL [2] and Ginga-J [3]. Ginga-NCL
performs declarative application written in Nested Context
INTRODUCTION Language (NCL) while Ginga-J can perform procedural
Digital television has created new services, products, application based on JavaTM known as Xlets [4].
contents, channels and business models. The Brazilian This paper proposes an extension to Ginga middleware
Digital TV System allows high quality audio and video, as through implementation of a new module incorporated to
well as interactivity, creating different contents for users. Ginga Common Core called Recommender. The
There are two main implications with Brazil Digital TV Recommender module is responsible for gathering, storing,
System: the increase of the number of channels being processing and recommending TV education programs. To
broadcasted with the same bandwidth and the possibility of develop the Recommender module, Ginga-NCL middleware
sending multiplexed applications with the audio-visual developed by PUC-RIO (Pontifical Catholic University of
content. As new channels emerge due to the transmission Rio de Janeiro) was used, implemented in C/C++ language
increase, it is necessary to create ways that allow the TV with source code available under GPLv2 license and
viewers to search among these channels. according with the patterns defined by the Brazilian system
The Electronic Program Guide (EPG) helps the TV digital television [4].
viewers. However, as new channels are available, an TVDI IN BRAZIL AND EDUCATION
information overload is unavoidable making the EPG system
inappropriate. In Shangai [1], a big city in China, the TV One of the reasons to implement TVDi in the national
operators provide different services (in the analogical territory is its potential to social inclusion. In Brazil, in many
system, channels), and this number has been increasing at a cases, the open TV is the only source of information for
20% rate per year. Thus, the traditional EPG system became people who do not frequently read newspaper, magazine or
unattractive because it takes too long for the viewers to any other kind of printed media. If we consider that the
search among hundreds of options available to find their access to written information is low and that the information
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transmitted through TV newscasts is the biggest link features anymore due particularly to the financial survival of
between the world and the daily routine of Brazilian people, theses broadcastings. It is possible to note, according to
we have many reasons not to ignore the reach power of this legislation, that the programming only admits transmission
technology. If it is correctly and consciously explored, with of programs with educative-cultural purposes. However,
the help of interactive resources, TVDi can represent a there is the option to recreational, informative or sport
powerful tool to have access to differentiated educational programs considered educative-cultural since they present
knowledge at the same time it can include Brazilian citizens instructive elements or educative-cultural focus identified in
digitally excluded nowadays. Thus, it can be said that in its presentation.
Brazil, the access to the Internet is low and high-class people Digital TV implantation in Brazil has been advancing. Some
are those who have more access to it and participate obstacles – among them the situation of commercial
somehow in the educational scenario. The low number of broadcastings, political interests, influences (and models) of
personal computers and the high number of TV sets in digital television international systems, legislation ruling the
Brazilian houses defend the efforts to use all TVDi potential radio broadcasting – still prevent its complete operation, but
in issues in the educational extent. If public policies are well when it is defined, a social participation never seen before in
structured, TVDi can reinforce a new educational paradigm, other historical moments can take place in Brazil, ensuring
allowing the entire population to have access to Internet access to information and culture. [6]
resources, video, images, sounds, interactivity to introduce
new knowledge, entertainment, education, leisure, services. RELATED WORKS
It can allow the unlimited access to written and audiovisual There are several recommendation systems for DTV (Digital
information. As the great part of Brazilian population has a Television) designed to offer a distinct personalization
limited access to information and Internet, and considering service and to help TV viewers to deal with the great
the fact that the TV is the durable good which is in almost all quantity of TV programs. Some systems related to the
Brazilian houses, we can consider the TVDi a way to current work are presented here.
significantly change the perspective of Brazilian distance The AIMED system proposed by [7], presents a
learning. Even knowing that the TVDi inclusion in Brazil recommendation mechanism that considers some TV viewer
will not solve the social inclusion problem, it is certain that characteristics as activities, interests, mood, TV use
all its power can improve the digital inclusion, for it will background and demographic information. These data are
ensure the information access, services and education to inserted in a neural network model that infers the viewers’
people with low purchasing power. [5] preferences about the programs. Unlike the work proposed
EDUCATIVE BROADCASTING IN BRAZIL in this paper, which uses the implicit data collection, in the
AIMED system, the data are collected and the system is set
According to the Communication Department, educative trough questionnaires. This approach is doubtful, mainly
broadcasting is the Sound Broadcasting Service (radio) or when limitations imposed to data input in a DTV system are
Sounds and Images Services (TV) intended for the considered.
transmission of educative-cultural programs which, besides In [8] a method to discover models of multiuser environment
performing together with teaching systems of any level or in intelligent houses based on users’ implicit interactions is
modality, aims the basic and higher education, the presented. This method stores information in logs. So, the
permanent education and the professional education, besides logs can be used by a recommendation system in order to
comprehending educational, cultural, pedagogical and decrease effort and adapt the content for each TV viewer as
professional orientation activities. The execution of well as for multiuser situations. Evaluating the TV viewers’
broadcasting services with exclusively educative purposes is background of 20 families, it was possible to see that the
granted to legal entities with internal public right, including accuracy of the proposed model was similar to an explicit
universities, which will be given the preference to obtain the system. This shows that collecting the data in an implicit
grant, and foundations privately established and others way is as efficient as the explicit approach. In this system,
Brazilian universities. the user has to identify himself in an explicit way, using the
The first educative broadcasting station, the University TV remote control. Unlike this system, the proposal in this paper
of Pernambuco pertaining to the Education Department, was aims at promoting services to the recommendation systems
on TV in 1967. Until 1980´s, educative TV broadcasting in for a totally implicit multiuser environment.
Brazil gave priority to essentially educative programs and in In [9], a program recommendation strategy for multiple TV
1997, the Brazilian Association of Public, Educative and viewers is proposed based on the combination of the
Cultural Broadcasting (ABEPEC) was created. In 1999, the viewer’s profile. The research analyzed three strategies to
participant broadcastings created the RPTV (Public TV perform the content recommendation and provided the
Network) which aims at establishing a common and choice of the strategy based on the profile combination. The
mandatory programming guide to the associated results proved that the TV viewers’ profile combination can
broadcastings. Today, the programming is different from reflect properly in the preferences of the majority of the
that one in the beginning of educative broadcasting members in a group. The proposal in this paper uses an
transmissions, that is, it does not have the strict educative approach similar to a multiuser environment, however,
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besides the profile combination, the time and day of the a set of modules responsible for the data processing,
week are also considered. information filtering in the transport stream. It is the
In [1] a personalized TV system is proposed loaded in the architecture core; Stack protocol layer responsible for
STB compatible with the Multimidia Home Plataform supporting many communication protocols like HTTP, RTP
(MHP) model of the digital television European pattern. and TS.
According to the authors, the system was implemented in a
commercial solution of the MHP middleware, and for that,
implemented alterations and inclusions of new modules in
this middleware. Offering recommendation in this system
requires two important information that must be available:
programs description and the viewer visualization behavior.
The description of the programs is obtained by
demultiplexing and decoding the information in the EIT
(Event Information Table) table. EIT is the table used to
transport specific information about programs, such as: start
time, duration and description of programs in digital
television environments. The viewing behavior is collected
monitoring the user action with the STB and the later
persistence of this information in the STB. The work of [1]
is similar to the work proposed in this paper. The implicit
collection of data, along with the inclusion of a new module FIGURE 1 – GINGA MIDDLEWARE ARCHITECTURE (ADJUSTED
in the middleware architecture, is an example of this WITH THE RECOMMENDATION SYSTEM)
similarity.
In [10], the Personalized Electronic Program Guide is The proposed system extends the Ginga middleware
considered a possible solution to the information overload functionalities including new services in the Ginga Common
problem, mentioned in the beginning of this work. The Core layer. The Recommender module is the main part of
authors compared the use of explicit and implicit profile and the recommendation system and it is inserted in the
proved that the indicators of implicit interests are similar to Common Core layer of Ginga-NCL architecture. The
the indicators of explicit interests. The approach to find out Recommender module is divided in two parts. The first one
the user’s profile in an implicit way is adopted in this work describes the components integrated to the source code of
and it is about an efficient mechanism in the context of the middleware such as Local Agent, Schedule Agent, Filter
television environment, where the information input is Agent and Data Agent. The second part describes the new
performed through remote control, a device that was not component added to the STB: Sqlite [13], a C library which
designed to this purpose. implements an attached relational database. Figure 2
In [11], the AVATAR recommendation system is presented, presents the Recommender module architecture.
compatible to the European MHP middleware. The authors I. Implemented Modules
propose a new approach, where the recommendation system
is distributed by broadcast service providers, as well as an This subsection describes the modules added to the Ginga-
interactive application. According to the authors, this NCL middleware source code and the extensions
approach allows the user to choose among different implemented to provide a better connection between
recommendation systems, what is not possible when we middleware and the recommendation system.
have an STB with a recommendation system installed in Local Agent is the module responsible for constant
plant. The AVATAR system uses the approach of implicit monitoring of the remote control. Any interaction between
collection of user profile and proposes modifications in the the viewer and the control is detected and stored in the
MHP middleware to include the monitoring method. The database. The Local Agent is essential for the
Naïve Bayes [12] is used as a classification algorithm and recommendation system that uses implicit approach to
one of the main reasons for that is the low use of STB perform the profile.
resources.
Scheduler Agent is the module responsible for periodically
SYSTEM OVERVIEW request the data mining. Data mining is a process that
demands time and processing, making its execution
The recommendation system proposed in this paper is based
impracticable every time the viewer requests a
on Ginga middleware. As mentioned before, the version
recommendation. Scheduler Agent module guarantees a new
used was the open source version of Ginga-NCL
processing every 24 hours preferably at night, when the STB
middleware. Figure 1 presents its architecture consisting of
is in standby.
three layers:
Resident applications responsible for the exhibition
(frequently called presentation layer); Ginga Common Core,
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items. For example, the system can be used to create a top-
10 question topic; the students would classify extra material
with a grade and the best extra materials would be
recommended. It would be also possible to have a top-10
favorite and a top-10 best students. Moreover, the system
could also provide a way to look for old content interesting
for the user to improve what is being studied at that moment.
METHODOLOGY AND TESTS
User history and EPG data are necessary to perform the
tests. These data were provided by IBOPE (Brazilian
Institute of Public Opinion and Statistics) [14] through a
treatment process almost entirely manual in order to be in
accordance to the standard format which must be used in the
Brazilian digital TV system and also in the tests.
Many technologies have been arising with the aim at
identifying behavior standards and its application in the
FIGURE 2 – RECOMMENDER MODULE ARCHITECTURE personalization. The recommendation systems operation is
found on these techniques and the most used are the
Collaborative Filtering and Content-Based Filtering which
Mining Agent is the module that accesses the information in includes several algorithms for each one. A
the viewer’s behavior background and the programming data recommendation system can use only one technique or two
from the EIT and SDT tables stored in cache to perform the together, becoming a hybrid system.
data mining. In order to process the data mining, the Mining In order to study, analyze and choose an algorithm to be
module has direct access to the database and recovers the used in Technical module, some information filtering
TV viewer’s behavior background. From the point of view algorithms were tested. The tests were performed in three
of the system performance, this communication between steps. In the first step, tests were performed with Apriori
mining module and user database is important. Without this algorithm. In the second step, the forecast method was used,
communication, it would be necessary to implement a new applying Cosine as measure of similarity. The third step was
module responsible for recover the database information and to compare the results and the operation with both
then make such data available to the mining algorithm. The algorithms, analyzing the facilities and difficulties,
second data set necessary to make possible the data mining especially for the implementation.
is the program guide. The program guide is composed by The association techniques algorithms identify
information sent by providers through EIT and SDT tables. associations between the data registers which are related in
These tables are stored in cache and are available to be some way. The basic premise finds elements which imply
recovered and processed by the Mining module. Ginga-NCL the presence of others in a same operation aiming at
Middleware does not implement storage mechanism in cache determining which are related. The association rules
of EIT and SDT tables. This functionality was implemented interconnect objects trying to show characteristics and
by the Recommender system. tendencies. The association discoveries present trivial and
non trivial association. The data was adapted in order to be
Filter Agent & Data Agent The raw data returned by the used in Apriori algorithm, that is, it was submitted to a pre-
Mining Agent module need to be filtered and later stored in processing phase. The user history was created from IBOPE
the viewer’s database. The Filter Agent and Data Agent data. For the implementation, it is not necessary that the data
modules are responsible for this function. The Filter Agent go through adjustments, as it will be collected in the correct
module receives the data from the mining provided by the format to be used. The results were satisfactory verifying
Mining Agent and eliminates any information that is not that Apriori can be applied to the system for it can be
important keeping only those which are relevant to the adapted to the system needs. [15, 16]
recommendation system such as the name of the program, The Cosine is a similarity measure, a forecast method
time, date, service provider and the name of the service. The which calculates the similarity between items and users,
Data Agent module receives the recommendations and stores consults similar items to a given item and matches item
them in the viewer’s database. content and user profile. The data also had to be adjusted to
be used with Cosine. Database in sqlite was used with the
If there were many educative programs on open TV, it EPG and the user history. From these two tables, it was
would be very useful to recommend other educative possible to derive two more, one with the profile of the
programs. However, “educative” is one of the many TV program watched by the user and other with the profile of
program categories. The system can be used inside a genres. It was necessary that the EPG passed through a
distance learning system to recommend several types of modification which should also occur in the implementation.
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A new table was created, identical to the EPG table, but
added with fields containing the genres names. According to
the adjustment of the program in the genres, these fields
were populated with 0 or 1, becoming a matrix. From these
tables it was possible to find the Cosine for the programs and
genres, the profile and what could be recommended to the
user. The results from the Cosine were also satisfactory
confirming that this technique can be applied to the system
for it can be adjusted to the system needs. [17, 18]
ANALYSIS
During the tests, it was possible to note some
particularities. Our system recommends contents based on
the programs genres and our analyses were performed
according to this standard. With Apriori algorithm, the data
are collected in the correct format to be used. For the Cosine, FIGURE 3. ACCURACY OF THE RECOMMENDATION SYSTEM
the EPG needs to be changed to a matrix before starting the
process of discovering profiles and recommendations. Figure 3 presents the results obtained after 4 weeks of
In a desktop, the feedback of the Cosine calculation is monitoring considering the best value obtained among the 8
faster in relation to the feedback of Apriori association rules. schools analyzed. It is clear that on the first weeks, as the
However, further studies about these algorithms processing collected data were few, Apriori algorithm did not extract
in these devices are still being performed. Apriori is able to relevant information from the preferences of the group. With
discover the profile from the standards, but to select the the data increase in the visualization background on the third
programs to be recommended, another technique must be and fourth week, the algorithm obtained better results and
used and the Cosine can find both the profile and the the index of recommendation acceptance increased.
recommendations.
The Cosine cannot discover these characteristics, but
reaches our goal. In order to discover behaviors similar to
the association rules, it is necessary to consult the databank.
Apriori output must be operated in order to give the correct
user profile, that is, the rules must be understood, and that is
very hard concerning implementation. The Cosine output is
clearer; the result straightly reaches intended goal, allowing
the output to be used without the need of a post-treatment.
Regarding the input, there is no need of treatment for
Apriori, since all data will be used as they are collected.
However, for the Cosine, whenever the EPG is updated, the
table containing the EPG matrix must be changed according
to the new EPG, becoming something hard to work. The
profile of the genres founded by both algorithms is similar.
RESULTS
In order to measure the evolution of the recommendation FIGURE 4 ACCURACY OF THE RECOMMENDATION SYSTEM PER
SCHOOL
offered to the students viewer, the following formula was
applied:
Figure 4 presents the accuracy per school. The main
Ef = (α / β) 100 (1) characteristic of the schools is the socioeconomics difference
among them. The conclusion is that Apriori algorithm had a
Where Ef is the efficacy of the recommendation system, good performance unrestricted to the students’
ranging from 0 to 100, α is the recommendation number ´socioeconomic profile.
accepted by the students viewers and β is the number of
recommendation presented. In order to monitor these data
provided by IBOPE were used. The validation adopted an
accuracy formula presented in (1).
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