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
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.
Key-words - Personalization, Multimedia, Recommendation
System, Digital TV, Middleware Ginga.
INTRODUCTION
favorite program. In face of this situation, personalized
recommendation systems are necessary.
Different from EPG functions which allow basic search,
a personalized TV system can create a profile for each TV
viewer and recommend programs that best match this
profile, avoiding the search in many EPG options to find the
favorite program. Elementary and secondary education
schools and universities generally seek to explore this new
model offering personalized content to their students. In this
context, a recommendation system is able to analyze the
profile of a group of students, suggesting the educational
content that best suits the needs of the group.
To make the benefits (new channels, interactive
applications) offered by the digital system possible, the TV
viewers with analogical system need new equipment called
set-top box (STB). STB is a device which works connected
to the TV and converts the digital sign received from the
provider to audio/video that the analogical TV can exhibit.
To have the advantages offered by the digital TV, the STB
needs a software layer which connects the hardware to the
interactive applications called middleware. The DTV
Brazilian System middleware is Ginga [2,3]. It allows
declarative and procedural applications through its
components Ginga-NCL [2] and Ginga-J [3]. Ginga-NCL
performs declarative application written in Nested Context
Language (NCL) while Ginga-J can perform procedural
application based on JavaTM known as Xlets [4].
This paper proposes an extension to Ginga middleware
through implementation of a new module incorporated to
Ginga Common Core called Recommender. The
Recommender module is responsible for gathering, storing,
processing and recommending TV education programs. To
develop the Recommender module, Ginga-NCL middleware
developed by PUC-RIO (Pontifical Catholic University of
Rio de Janeiro) was used, implemented in C/C++ language
with source code available under GPLv2 license and
according with the patterns defined by the Brazilian system
digital television [4].
Digital television has created new services, products,
contents, channels and business models. The Brazilian
Digital TV System allows high quality audio and video, as
well as interactivity, creating different contents for users.
There are two main implications with Brazil Digital TV
System: the increase of the number of channels being
broadcasted with the same bandwidth and the possibility of
sending multiplexed applications with the audio-visual
content. As new channels emerge due to the transmission
increase, it is necessary to create ways that allow the TV
viewers to search among these channels.
The Electronic Program Guide (EPG) helps the TV
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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October 27 - 30, 2010, Washington, DC
40th ASEE/IEEE Frontiers in Education Conference
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2. Session S1E
transmitted through TV newscasts is the biggest link
between the world and the daily routine of Brazilian people,
we have many reasons not to ignore the reach power of this
technology. If it is correctly and consciously explored, with
the help of interactive resources, TVDi can represent a
powerful tool to have access to differentiated educational
knowledge at the same time it can include Brazilian citizens
digitally excluded nowadays. Thus, it can be said that in
Brazil, the access to the Internet is low and high-class people
are those who have more access to it and participate
somehow in the educational scenario. The low number of
personal computers and the high number of TV sets in
Brazilian houses defend the efforts to use all TVDi potential
in issues in the educational extent. If public policies are well
structured, TVDi can reinforce a new educational paradigm,
allowing the entire population to have access to Internet
resources, video, images, sounds, interactivity to introduce
new knowledge, entertainment, education, leisure, services.
It can allow the unlimited access to written and audiovisual
information. As the great part of Brazilian population has a
limited access to information and Internet, and considering
the fact that the TV is the durable good which is in almost all
Brazilian houses, we can consider the TVDi a way to
significantly change the perspective of Brazilian distance
learning. Even knowing that the TVDi inclusion in Brazil
will not solve the social inclusion problem, it is certain that
all its power can improve the digital inclusion, for it will
ensure the information access, services and education to
people with low purchasing power. [5]
EDUCATIVE BROADCASTING IN BRAZIL
According to the Communication Department, educative
broadcasting is the Sound Broadcasting Service (radio) or
Sounds and Images Services (TV) intended for the
transmission of educative-cultural programs which, besides
performing together with teaching systems of any level or
modality, aims the basic and higher education, the
permanent education and the professional education, besides
comprehending educational, cultural, pedagogical and
professional orientation activities. The execution of
broadcasting services with exclusively educative purposes is
granted to legal entities with internal public right, including
universities, which will be given the preference to obtain the
grant, and foundations privately established and others
Brazilian universities.
The first educative broadcasting station, the University TV
of Pernambuco pertaining to the Education Department, was
on TV in 1967. Until 1980´s, educative TV broadcasting in
Brazil gave priority to essentially educative programs and in
1997, the Brazilian Association of Public, Educative and
Cultural Broadcasting (ABEPEC) was created. In 1999, the
participant broadcastings created the RPTV (Public TV
Network) which aims at establishing a common and
mandatory programming guide to the associated
broadcastings. Today, the programming is different from
that one in the beginning of educative broadcasting
transmissions, that is, it does not have the strict educative
features anymore due particularly to the financial survival of
theses broadcastings. It is possible to note, according to
legislation, that the programming only admits transmission
of programs with educative-cultural purposes. However,
there is the option to recreational, informative or sport
programs considered educative-cultural since they present
instructive elements or educative-cultural focus identified in
its presentation.
Digital TV implantation in Brazil has been advancing. Some
obstacles – among them the situation of commercial
broadcastings, political interests, influences (and models) of
digital television international systems, legislation ruling the
radio broadcasting – still prevent its complete operation, but
when it is defined, a social participation never seen before in
other historical moments can take place in Brazil, ensuring
access to information and culture. [6]
RELATED WORKS
There are several recommendation systems for DTV (Digital
Television) designed to offer a distinct personalization
service and to help TV viewers to deal with the great
quantity of TV programs. Some systems related to the
current work are presented here.
The AIMED system proposed by [7], presents a
recommendation mechanism that considers some TV viewer
characteristics as activities, interests, mood, TV use
background and demographic information. These data are
inserted in a neural network model that infers the viewers’
preferences about the programs. Unlike the work proposed
in this paper, which uses the implicit data collection, in the
AIMED system, the data are collected and the system is set
trough questionnaires. This approach is doubtful, mainly
when limitations imposed to data input in a DTV system are
considered.
In [8] a method to discover models of multiuser environment
in intelligent houses based on users’ implicit interactions is
presented. This method stores information in logs. So, the
logs can be used by a recommendation system in order to
decrease effort and adapt the content for each TV viewer as
well as for multiuser situations. Evaluating the TV viewers’
background of 20 families, it was possible to see that the
accuracy of the proposed model was similar to an explicit
system. This shows that collecting the data in an implicit
way is as efficient as the explicit approach. In this system,
the user has to identify himself in an explicit way, using the
remote control. Unlike this system, the proposal in this paper
aims at promoting services to the recommendation systems
for a totally implicit multiuser environment.
In [9], a program recommendation strategy for multiple TV
viewers is proposed based on the combination of the
viewer’s profile. The research analyzed three strategies to
perform the content recommendation and provided the
choice of the strategy based on the profile combination. The
results proved that the TV viewers’ profile combination can
reflect properly in the preferences of the majority of the
members in a group. The proposal in this paper uses an
approach similar to a multiuser environment, however,
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October 27 - 30, 2010, Washington, DC
40th ASEE/IEEE Frontiers in Education Conference
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3. Session S1E
besides the profile combination, the time and day of the
week are also considered.
In [1] a personalized TV system is proposed loaded in the
STB compatible with the Multimidia Home Plataform
(MHP) model of the digital television European pattern.
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
in the middleware architecture, is an example of this
similarity.
In [10], the Personalized Electronic Program Guide is
considered a possible solution to the information overload
problem, mentioned in the beginning of this work. The
authors compared the use of explicit and implicit profile and
proved that the indicators of implicit interests are similar to
the indicators of explicit interests. The approach to find out
the user’s profile in an implicit way is adopted in this work
and it is about an efficient mechanism in the context of
television environment, where the information input is
performed through remote control, a device that was not
designed to this purpose.
In [11], the AVATAR recommendation system is presented,
compatible to the European MHP middleware. The authors
propose a new approach, where the recommendation system
is distributed by broadcast service providers, as well as an
interactive application. According to the authors, this
approach allows the user to choose among different
recommendation systems, what is not possible when we
have an STB with a recommendation system installed in
plant. The AVATAR system uses the approach of implicit
collection of user profile and proposes modifications in the
MHP middleware to include the monitoring method. The
Naïve Bayes [12] is used as a classification algorithm and
one of the main reasons for that is the low use of STB
resources.
SYSTEM OVERVIEW
The recommendation system proposed in this paper is based
on Ginga middleware. As mentioned before, the version
used was the open source version of Ginga-NCL
middleware. Figure 1 presents its architecture consisting of
three layers:
Resident applications responsible for the exhibition
(frequently called presentation layer); Ginga Common Core,
a set of modules responsible for the data processing,
information filtering in the transport stream. It is the
architecture core; Stack protocol layer responsible for
supporting many communication protocols like HTTP, RTP
and TS.
FIGURE 1 – GINGA MIDDLEWARE ARCHITECTURE (ADJUSTED
WITH THE RECOMMENDATION SYSTEM)
The proposed system extends the Ginga middleware
functionalities including new services in the Ginga Common
Core layer. The Recommender module is the main part of
the recommendation system and it is inserted in the
Common Core layer of Ginga-NCL architecture. The
Recommender module is divided in two parts. The first one
describes the components integrated to the source code of
the middleware such as Local Agent, Schedule Agent, Filter
Agent and Data Agent. The second part describes the new
component added to the STB: Sqlite [13], a C library which
implements an attached relational database. Figure 2
presents the Recommender module architecture.
I.
Implemented Modules
This subsection describes the modules added to the GingaNCL middleware source code and the extensions
implemented to provide a better connection between
middleware and the recommendation system.
Local Agent is the module responsible for constant
monitoring of the remote control. Any interaction between
the viewer and the control is detected and stored in the
database. The Local Agent is essential for the
recommendation system that uses implicit approach to
perform the profile.
Scheduler Agent is the module responsible for periodically
request the data mining. Data mining is a process that
demands time and processing, making its execution
impracticable every time the viewer requests a
recommendation. Scheduler Agent module guarantees a new
processing every 24 hours preferably at night, when the STB
is in standby.
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4. Session S1E
items. For example, the system can be used to create a top10 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
FIGURE 2 – RECOMMENDER MODULE ARCHITECTURE
Mining Agent is the module that accesses the information in
the viewer’s behavior background and the programming data
from the EIT and SDT tables stored in cache to perform the
data mining. In order to process the data mining, the Mining
module has direct access to the database and recovers the
TV viewer’s behavior background. From the point of view
of the system performance, this communication between
mining module and user database is important. Without this
communication, it would be necessary to implement a new
module responsible for recover the database information and
then make such data available to the mining algorithm. The
second data set necessary to make possible the data mining
is the program guide. The program guide is composed by
information sent by providers through EIT and SDT tables.
These tables are stored in cache and are available to be
recovered and processed by the Mining module. Ginga-NCL
Middleware does not implement storage mechanism in cache
of EIT and SDT tables. This functionality was implemented
by the Recommender system.
Filter Agent & Data Agent The raw data returned by the
Mining Agent module need to be filtered and later stored in
the viewer’s database. The Filter Agent and Data Agent
modules are responsible for this function. The Filter Agent
module receives the data from the mining provided by the
Mining Agent and eliminates any information that is not
important keeping only those which are relevant to the
recommendation system such as the name of the program,
time, date, service provider and the name of the service. The
Data Agent module receives the recommendations and stores
them in the viewer’s database.
If there were many educative programs on open TV, it
would be very useful to recommend other educative
programs. However, “educative” is one of the many TV
program categories. The system can be used inside a
distance learning system to recommend several types of
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
personalization. The recommendation systems operation is
found on these techniques and the most used are the
Collaborative Filtering and Content-Based Filtering which
includes several algorithms for each one.
A
recommendation system can use only one technique or two
together, becoming a hybrid system.
In order to study, analyze and choose an algorithm to be
used in Technical module, some information filtering
algorithms were tested. The tests were performed in three
steps. In the first step, tests were performed with Apriori
algorithm. In the second step, the forecast method was used,
applying Cosine as measure of similarity. The third step was
to compare the results and the operation with both
algorithms, analyzing the facilities and difficulties,
especially for the implementation.
The association techniques algorithms identify
associations between the data registers which are related in
some way. The basic premise finds elements which imply
the presence of others in a same operation aiming at
determining which are related. The association rules
interconnect objects trying to show characteristics and
tendencies. The association discoveries present trivial and
non trivial association. The data was adapted in order to be
used in Apriori algorithm, that is, it was submitted to a preprocessing phase. The user history was created from IBOPE
data. For the implementation, it is not necessary that the data
go through adjustments, as it will be collected in the correct
format to be used. The results were satisfactory verifying
that Apriori can be applied to the system for it can be
adapted to the system needs. [15, 16]
The Cosine is a similarity measure, a forecast method
which calculates the similarity between items and users,
consults similar items to a given item and matches item
content and user profile. The data also had to be adjusted to
be used with Cosine. Database in sqlite was used with the
EPG and the user history. From these two tables, it was
possible to derive two more, one with the profile of the
program watched by the user and other with the profile of
genres. It was necessary that the EPG passed through a
modification which should also occur in the implementation.
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5. Session S1E
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,
the EPG needs to be changed to a matrix before starting the
process of discovering profiles and recommendations.
In a desktop, the feedback of the Cosine calculation is
faster in relation to the feedback of Apriori association rules.
However, further studies about these algorithms processing
in these devices are still being performed. Apriori is able to
discover the profile from the standards, but to select the
programs to be recommended, another technique must be
used and the Cosine can find both the profile and the
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.
FIGURE 3. ACCURACY OF THE RECOMMENDATION SYSTEM
Figure 3 presents the results obtained after 4 weeks of
monitoring considering the best value obtained among the 8
schools analyzed. It is clear that on the first weeks, as the
collected data were few, Apriori algorithm did not extract
relevant information from the preferences of the group. With
the data increase in the visualization background on the third
and fourth week, the algorithm obtained better results and
the index of recommendation acceptance increased.
RESULTS
In order to measure the evolution of the recommendation
offered to the students viewer, the following formula was
applied:
(1)
Ef = (α / β) 100
Where Ef is the efficacy of the recommendation system,
ranging from 0 to 100, α is the recommendation number
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).
FIGURE 4 ACCURACY OF THE RECOMMENDATION SYSTEM PER
SCHOOL
Figure 4 presents the accuracy per school. The main
characteristic of the schools is the socioeconomics difference
among them. The conclusion is that Apriori algorithm had a
good performance unrestricted to the students’
´socioeconomic profile.
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October 27 - 30, 2010, Washington, DC
40th ASEE/IEEE Frontiers in Education Conference
S1E-5

6. Session S1E
[3]
[4]
[5]
[6]
FIGURE 5 – RECOMMENDERTV SYSTEM
[7]
Figure 5 shows Recommender system. The application
used as front-end is written in NCL and allows the students
to search the recommendation list selecting the education
program.
CONCLUSION
With the appearance of digital TV, a variety of new
services (in the analogical system, channels) will be
available. This information overload requires the
implementation of new mechanisms to offer facilities to the
students looking for their education programs. These new
mechanisms suggesting the viewers programs are known as
recommendation systems. A recommendation system
compatible with Ginga middleware is presented in this paper
and it is implemented according to the standards of the
digital television Brazilian system. The recommendation
system was modeled considering the current characteristics
of the television, and this model can be adjusted to other
standards and also to new portable devices which will be on
the market. At last, future works can include algorithms of
collaborative filtering and also a new architecture using
client-server, providing and offering other kinds of
personalization services for the users.
ACKNOWLEDGMENT
We thank IBOPE for providing real data about the
electronic program guide and also the viewer’s behavior data
from March, 05, 2009 to March, 19, 2009.
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
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October 27 - 30, 2010, Washington, DC
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