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Using Content-Based Filtering in a System of
Recommendation in the Context of Digital Mobile
Elaine Cecília Gatto, Sergio Donizetti Zorzo
IEEE Conference Publishing
Computer Science Department. Federal University of São Carlos – UFSCar
Highway Washington Luís, Km 235, PO Box 676, 13565-9. São Carlos, São Paulo, Brazil.
Abstract-Recommendation systems provide suggestions based
on information about the preferences of users. The filtering
information is used by recommender systems for the processing
of information and suggestions to users and content-based
filtering is an approach to filtering information widely used in
recommender systems. Content-Based Filtering on analyzing the
correlation of the content of items with the profile, suggesting
relevant items and discarding the irrelevant. Widely used on the
Internet, recommendation systems are being studied for use in
the context of Digital TV, there are already several studies in this
direction. Just as occurs on the Internet, recommendation
systems can be used in Digital TV for recommendation of TV
programs, advertising and publicity and also electronic
commerce. Thus, the items in the context of digital TV, may be
programs, publicity / advertising and the products to be sold.
Applying Content Filtering Based on the recommendation of
programs, for example, it should correlate the content of these
programs with user preferences, which in this scenario are the
types of programs he has preferred to watch. This paper presents
the studies performed with Content Filtering Based on Data
Applied to Digital TV. The studies seek to observe and evaluate
how some techniques of content-based filtering can be used in
recommendation systems in the context of Digital TV.
Digital TV implementation in Brazil provides new markets
which can be explored. Well-succeeded technologies as those
in Web environment, for example, can be applied in Digital
TV domain and achieve the same success.
The interaction either through the remote control or the cell
phone keyboard etc by the user today, will allow many
applications to be carried to this environment.
One of the areas which has been extensively studied and is
well-succeeded in the Web is that of personalization. There
are some surveys concerning recommendation systems for
Digital TV as for example [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] among
Recommendation systems can contribute to a better use of
Digital TV in residences, in groups or individually, in a cell
phone, for example. These systems can help the user to choose
the program, avoiding waste of time and of course, suggesting
to the user programs which really interest him. Moreover,
recommendation systems can be applied to publicity and
advertisement on Digital TV, as well as in the T-Commerce.
This work is organized as follows: section 1 introduces the
paper, section 2 presents a comparison between digital TV and
portable devices for homes, section 3 presents related works,
section 4 talks about content-based filtering, section 5 presents
our recommendation system, Section 6 talks about the
characteristics of households, the EPG, the user history and
methodology used for the tests, Section 7 talks about the
results and Section 8 presents the conclusion.
II. COMPARING IDTV IN RESIDENCES AND IDTV FOR CELL
The use of IDTV for cell phones will quickly boom due to
the increasingly quantity of these devices surpassing television
sets in Brazil, when cell phones with IDTV are available to
population. Thus, some differences between IDTV for
residences and for cell phones can be noticed.
IDTV standard adopted in Brazil calls full-seg the fixed
devices like set-top-box, and one-seg, devices like cell phones,
miniTVs, PDAs, etc. In residences, the IDTV is used by all
residents while in the cell phone it is normally used by only
one user, the owner of the device.
Another characteristic is the size of the display. In
residences, the IDTV television sets have screens bigger than
30”, where is possible to have a more flexible development,
presentation and displaying of the content. However, cell
phones screens are smaller than 10" requiring a higher effort
in development to display the content on the screen avoiding
image pollution and confusion to the user.
An exceptional characteristic in this environment is that
IDTV for cell phones can be seen anywhere and anytime. On
the other hand, IDTV viewing period in residences can be
longer than in cell phones which are used in situations of
waiting and displacement.
IDTV in cell phones can use already existent 2G/3G net
architecture, and 4G in the future, as a return channel, making
interactivity possible in this environment before occurring in
The middleware adopted in Brazil has national technology
and is called Ginga. Ginga-NCL and Ginga-J declarative and
imperative portions of the middleware are necessary for full-
seg devices. For one-seg devices, only Ginga-NCL declarative
portion is required. There is a reference implementation of the
middleware for full-seg devices. For one-seg devices, this
reference implementation is not available yet, but are working
in this middleware development, as PUC-RIO and UFES
(Symbian e Android). [11, 12, 13]
The users of these devices need special attention due to
current characteristics of this environment like processing
power, storage capacity and battery.
III. RELATED WORK
There are many works involving recommendation systems
for IDTV for set-top-box and more recently for portable
devices. This section presents two recent works about
recommendation systems for IDTV.
In  the recommendation system fits the systems with
content-based filtering category, using text mining. The
system uses a simple interface with the user and accepts a
natural language as text entry as well as four values which
reflect user preferences for comedy, action, horror and erotic.
First, the system extracts texts and then searches for emotions
in the text and the distances among themes are calculated.
Finally, an index is calculated for each entry and a list of
programs organized by this index is returned.
In  the main aim of the system is substituting the
common content by a personalized and adapted content in a
more attractive way for the user. Therefore, this system
accepts and allows TV reception either through broadcast, or
multimedia streaming. The system uses explicit collection –
when using for the first time it is necessary to inform the
preferences – and also implicit collection – user’s actions in
the device are monitored, stored and sent to the server. The
personalized content– chosen based on preferences – is sent to
the user’s portable device by the Server in order to be
previously stored before being exhibited.
The ZapTV  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
Technologies emerging from Web 2.0 and involved in the
Semantic Web. The main functionalities of ZapTV include a
social net, personalized content broadcasting (implicit or
explicit recommendation), thematic channels diffusion
planning (age-group, 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.
Our recommendation system is in the portable device, and
the inclusion of servers in Brazilian IDTV architecture for cell
phones is not necessary to provide recommendation and,
consequently the need for remote communication is also not
necessary, avoiding that the user pay for the data traffic in the
net in order to receive recommendation or send his data, and
thus, protecting the user’s data privacy.
IV. CONTENT-BASED FILTERING
Content-Based Filtering (CBF) uses the content attributes to
describe the content of the items and then calculate the
similarity. This approach does not depend on other users’
evaluation about the items. CBF is an information recovery
technique which bases its forecast on the fact that previous
preferences of the users are reliable indicators for future
behavior. In order to formulate recommendations, a variety of
algorithms has been proposed to evaluate the content of
documents and find regularities. Some of these algorithms
operate with classification knowledge and others operate with
the problem of regression. Some of the problems and
limitations found in systems using CBF are super
specialization, the problem of the new user and the analyses of
limited content. [7, 6, 14]
V. RECOMMENDER SYSTEM
Our recommendation system aims to facilitate the IDTV
user’s routine by interacting with a simple interface which
provides content of preference without spending so much time
to find it.
The process starts when the user turns on the TV in the cell
phone. The user history data collected is submitted to
information filtering based on content in order to find the
user’s profile. Data resulting from this process are formatted.
The user profile is stored in a database with the date and time
of the generation. With the user profile updated it is possible
to look in the EPG for compatible TV programs and which are
going to be transmitted around the current time, providing a
list of these programs. This list is also stored in a data base
with the date and time of generation.
The recommendations are presented to the user and those
required are stored with the user history. During the time the
IDTV for cell phone is turned on, all programs viewed by the
user are stored in the database which has the user history. This
process is repeated every time the user turns on the TV.
Ginga-NCL middleware has a layer for resident applications
responsible for exhibition, other layer for the common center
responsible for offering several services and a last layer
regarding the protocols stack.
The recommendation system is considered as an element in
Ginga-NCL architecture, in Ginga Common Core, due to the
need to continue using data locally and also use Tunner
libraries – in order to obtain information about the channels
tune – ESInformation – in order to obtain information about
EIT table generating the EPG – and Context Manager – to
obtain system information.
As GINGA-NCL middleware is mandatory in Brazil for
portable devices, the recommendation system was planned,
designed and modeled according to Brazilian rules which refer
to portable devices, thus meeting these devices needs. More
details on our system can be obtained in. 
For the tests we used the data corresponding to TV viewing
and program schedule. These data were provided by IBOPE
which is a Brazilian multinational private equity firms and a
leading market research in Latin America. 67 years ago to
IBOPE provides a wide range of information and media
studies, public opinion, voting intention, consumption, brand
and market behavior. In the following subsections the
characteristics of these data and the tests will be detailed. 
A. Characteristics of Residences
Data that contain information provided IBOPE EPG (TV
programming), history of the user's view (what the viewer saw)
and also the socioeconomic information. All these data were
separated and stored in MySQL database the data correspond
to 15 days of programming and monitoring of six Brazilian
households with TV programming Open. These households
were monitored every minute, and each individual was also
NUMBER OF INDIVIDUALS BY RESIDENCE
B. Characteristics of Date
The data used for these tests have undergone a process of
manual adjustment. For each of the algorithms used, was a
necessary pre-processing for correct use and analysis.
Subsections C, D, E and F detail the composition of these data.
The EPG (Electronic Program Guide) is composed of 15
TXT files called programming files, one for each day
(05/03/2008 to 19/03/2008) with a grid of 10 TV stations
Open, starting at 00:00:00 and ends at 05:59:00. After
understanding the files that make up the EPG, the data were
copied from the archives of programming a spreadsheet
BrOffice and then was done cleaning up unnecessary data.
We noticed some inconsistencies in schedules, which were
immediately corrected so that future analysis will not generate
erroneous results. This was repeated for each of the 15
programming files, generating a single spreadsheet containing
the entire 15 days of EPG.
Was added to these data the day of week and duration of the
program. The EPG, this step is not complete, missing the
genre and subgenre of each program. Searched for it on the
official websites of the gender of each station broadcast
programs and then identified according to the Brazilian
standard ABNT NBR 15603-3:2007, Annex C, "gender
descriptor in the descriptor content." 
A new table was created, identical to the EPG table, but
with added fields with the names of genre, to be used with the
technique of the cosine. These fields were populated with 0 or
1 depending on the program or not fit in that genre, becoming
D. User History
Historical data viewing of users are needed for the
discovery of these preferences. In the context of digital TV
that we are considering, these data are collected and stored
Spreadsheets sent by tuning IBOPE, which contains user
data, were modified so that filtering techniques based on
content could be applied.
The data were then separated by households and although
some homes have more than one TV in these households it
was noticed that there is no record of monitoring more than
one TV at the same time and therefore it is considered that the
household has only one TV.
Data were also formatted: date in yyyy-mm-dd, time in
hh:mm:ss format TV and 00X. The resulting sheets were
converted to CSV files that were then inserted in MySQL
were also added to user data, information for day of week,
time of day and duration of display.
We simulate the Content Filtering Based on using two
different techniques, the Apriori and cosine, both using as a
target attribute to gender. In the case of Apriori, we apply the
settings shown in Figure 1.
Then start the simulation for Apriori. For each household,
the process was the same on the first day to generate
recommendations for the second day, the second day, based
on what was seen the day before and the present day, to
generate recommendations for the third day, and so on.
First we opened the CSV file corresponding to the X home
and day 1. After convert some attributes String to Numeric
Nominal and others for Nominal (applying filters). Then the
executable Apriori and ultimately save the output. With the
data saved, it was possible to assess whether the day after,
someone from the household attended any of the genera found
To find the cosine first and save the profile, then calculate
the distance of the cosine, cosine and the standard itself and
finally found the right answers. The process is iterative and is
performed for each day and each household.
F. Apriori e Cosine
The algorithms of association techniques identify
associations between data records that are somehow related.
The basic premise is evidence on the presence of others in the
same transaction, to determine what things are related.
Moreover, we also calculated the average percentage of
correct answers for the number of recommendations generated
using the following formula:
Figure 2 and Figure 3 show the chart with the average
achieved for the percentages calculated for each hit home for
the techniques of the Cosine and Apriori. Households 2, 4 and
6 had the best results for the cosine and the Apriori
households 4:06. As can be seen in Figure 4, which presents
the comparison chart between the two techniques in general,
the cosine has outperformed over the Apriori.
Figure 1. Parameters used in Apriori.
The association rules interconnect objects in an attempt to
expose patterns and trends. The discovery of associations must
show both associations as trivial associations not trivial.
The Apriori algorithm is often used to mine association
rules. Apriori can work with a high number of attributes,
generating various combinations between them and
performing successive searches across the database,
maintaining optimum performance in terms of processing time.
The algorithm tries to find all the relevant association rules
between items, which has the form X (history) ==> Y
(consequent). If x% of transactions that contain X also contain
Y, then x% represents the factor of trust (under the rule of
confidence). The support factor is a measure representing x%
of cases in which X and Y occur simultaneously over the total
number of records (often). 
The cosine is a measure of similarity, a metric that can be
applied to find out if an item has a correlation or not with the
user profile. A binary vector is a set of two elements, x and y.
In an n-dimensional space, where n is the number of items in
the vector. You can therefore calculate the cosine between the
vectors, measured as similarity between the user profile and its
history. The similarity is high when the value of cosine is high,
the closer to 1, the greater the similarity. 
Figure 2. Parameters used in Cosine.
Figure 3. Parameters used in Apriori.
For all households were made in excel spreadsheets to
account for the percentage of success of each technique to
each household. As the number of recommendations were
generated in May, then the basic formula for calculating the
percentage of correct answers was used:
Figure 4. Comparison between means of Apriori and Cosine.
During the tests, we observed some peculiarities. Our
system recommends content based on the kinds of programs,
and our analysis were made according to that parameter. With
the Apriori algorithm, the format of the data is already
collected correctly for use. For cosine, the EPG needs to be
changed to an array before starting the discovery process
profiles and recommendations.
The Apriori is able to mine only the user viewing history,
discovering your profile from the rules. To select the programs
to be recommended, another technique should be used. The
cosine can do both. However, Apriori can discover more
features in the user history, for example, "the user stands in
front of the TV more often at night, he enjoys watching
movies and watching TV more frequently in the second."
The cosine cannot find these features, but can reach our
goal. To find similar patterns in association rules, it is
necessary to use more complex queries to the bank.
The output from the Apriori must be crafted to generate the
correct profile of the user, ie, the rules should be interpreted,
which in terms of implementation becomes somewhat
cumbersome. The cosine output is more readable, its result
goes straight to the intended goal, allowing the output to be
used without the need for a post-treatment.
In relation to entry to the Apriori there is no need of
treatment, since the data will be used the way they are
collected. But for the cosine, where the EPG is updated, the
table containing the matrix of the EPG should be modified
according to the new EPG, becoming somewhat laborious.
Then simulated with both techniques the process of delivery
and acceptance of recommendations by calculating the
percentage of correct and generating graphics. The profile of
genres found by both algorithms are similar. Although both
techniques to cover the needs of the system, the cosine is one
that can be better utilized.
On a desktop, as was the case of our tests, the return of
calculating the cosine is faster in relation to the return of the
Apriori association rules. However, further studies on the
consumption of processing of these algorithms in a cell with
TVDI is not yet possible in Brazil. The time that the whole
process of recommendation takes to complete varies according
to the technique of customization to be used. In our tests and
simulations, the cosine ends the process before the Apriori.
Studies show that although both algorithms meet our needs,
these two techniques the Cosine can be better worked in the
recommendation system for TVDPI.
We thank IBOPE for providing real data about the
electronic program guide and also the viewer’s behavior data
from March, 05, 2008 to March, 19, 2008.
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