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

1. Using Content-Based Filtering in a System of
Recommendation in the Context of Digital Mobile
Interactive TV
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.
{elaine_gatto, zorzo}@dc.ufscar.br.
Abstract-Recommendation systems provide suggestions based This work is organized as follows: section 1 introduces the
on information about the preferences of users. The filtering paper, section 2 presents a comparison between digital TV and
information is used by recommender systems for the processing
of information and suggestions to users and content-based portable devices for homes, section 3 presents related works,
filtering is an approach to filtering information widely used in section 4 talks about content-based filtering, section 5 presents
recommender systems. Content-Based Filtering on analyzing the our recommendation system, Section 6 talks about the
correlation of the content of items with the profile, suggesting characteristics of households, the EPG, the user history and
relevant items and discarding the irrelevant. Widely used on the methodology used for the tests, Section 7 talks about the
Internet, recommendation systems are being studied for use in
the context of Digital TV, there are already several studies in this results and Section 8 presents the conclusion.
direction. Just as occurs on the Internet, recommendation
systems can be used in Digital TV for recommendation of TV II. COMPARING IDTV IN RESIDENCES AND IDTV FOR CELL
programs, advertising and publicity and also electronic PHONES
commerce. Thus, the items in the context of digital TV, may be
programs, publicity / advertising and the products to be sold. The use of IDTV for cell phones will quickly boom due to
Applying Content Filtering Based on the recommendation of the increasingly quantity of these devices surpassing television
programs, for example, it should correlate the content of these
programs with user preferences, which in this scenario are the sets in Brazil, when cell phones with IDTV are available to
types of programs he has preferred to watch. This paper presents population. Thus, some differences between IDTV for
the studies performed with Content Filtering Based on Data residences and for cell phones can be noticed.
Applied to Digital TV. The studies seek to observe and evaluate IDTV standard adopted in Brazil calls full-seg the fixed
how some techniques of content-based filtering can be used in devices like set-top-box, and one-seg, devices like cell phones,
recommendation systems in the context of Digital TV.
miniTVs, PDAs, etc. In residences, the IDTV is used by all
I. INTRODUCTION residents while in the cell phone it is normally used by only
one user, the owner of the device.
Digital TV implementation in Brazil provides new markets Another characteristic is the size of the display. In
which can be explored. Well-succeeded technologies as those residences, the IDTV television sets have screens bigger than
in Web environment, for example, can be applied in Digital 30”, where is possible to have a more flexible development,
TV domain and achieve the same success. presentation and displaying of the content. However, cell
The interaction either through the remote control or the cell phones screens are smaller than 10" requiring a higher effort
phone keyboard etc by the user today, will allow many in development to display the content on the screen avoiding
applications to be carried to this environment. image pollution and confusion to the user.
One of the areas which has been extensively studied and is An exceptional characteristic in this environment is that
well-succeeded in the Web is that of personalization. There IDTV for cell phones can be seen anywhere and anytime. On
are some surveys concerning recommendation systems for the other hand, IDTV viewing period in residences can be
Digital TV as for example [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] among longer than in cell phones which are used in situations of
others. waiting and displacement.
Recommendation systems can contribute to a better use of IDTV in cell phones can use already existent 2G/3G net
Digital TV in residences, in groups or individually, in a cell architecture, and 4G in the future, as a return channel, making
phone, for example. These systems can help the user to choose interactivity possible in this environment before occurring in
the program, avoiding waste of time and of course, suggesting IDTV.
to the user programs which really interest him. Moreover, The middleware adopted in Brazil has national technology
recommendation systems can be applied to publicity and and is called Ginga. Ginga-NCL and Ginga-J declarative and
advertisement on Digital TV, as well as in the T-Commerce. imperative portions of the middleware are necessary for full-

2. seg devices. For one-seg devices, only Ginga-NCL declarative phones is not necessary to provide recommendation and,
portion is required. There is a reference implementation of the consequently the need for remote communication is also not
middleware for full-seg devices. For one-seg devices, this necessary, avoiding that the user pay for the data traffic in the
reference implementation is not available yet, but are working net in order to receive recommendation or send his data, and
in this middleware development, as PUC-RIO and UFES thus, protecting the user’s data privacy.
(Symbian e Android). [11, 12, 13] IV. CONTENT-BASED FILTERING
The users of these devices need special attention due to
current characteristics of this environment like processing Content-Based Filtering (CBF) uses the content attributes to
power, storage capacity and battery. describe the content of the items and then calculate the
similarity. This approach does not depend on other users’
III. RELATED WORK
evaluation about the items. CBF is an information recovery
There are many works involving recommendation systems technique which bases its forecast on the fact that previous
for IDTV for set-top-box and more recently for portable preferences of the users are reliable indicators for future
devices. This section presents two recent works about behavior. In order to formulate recommendations, a variety of
recommendation systems for IDTV. algorithms has been proposed to evaluate the content of
In [5] the recommendation system fits the systems with documents and find regularities. Some of these algorithms
content-based filtering category, using text mining. The operate with classification knowledge and others operate with
system uses a simple interface with the user and accepts a the problem of regression. Some of the problems and
natural language as text entry as well as four values which limitations found in systems using CBF are super
reflect user preferences for comedy, action, horror and erotic. specialization, the problem of the new user and the analyses of
First, the system extracts texts and then searches for emotions limited content. [7, 6, 14]
in the text and the distances among themes are calculated. V. RECOMMENDER SYSTEM
Finally, an index is calculated for each entry and a list of
programs organized by this index is returned. Our recommendation system aims to facilitate the IDTV
In [3] the main aim of the system is substituting the user’s routine by interacting with a simple interface which
common content by a personalized and adapted content in a provides content of preference without spending so much time
more attractive way for the user. Therefore, this system to find it.
accepts and allows TV reception either through broadcast, or The process starts when the user turns on the TV in the cell
multimedia streaming. The system uses explicit collection – phone. The user history data collected is submitted to
when using for the first time it is necessary to inform the information filtering based on content in order to find the
preferences – and also implicit collection – user’s actions in user’s profile. Data resulting from this process are formatted.
the device are monitored, stored and sent to the server. The The user profile is stored in a database with the date and time
personalized content– chosen based on preferences – is sent to of the generation. With the user profile updated it is possible
the user’s portable device by the Server in order to be to look in the EPG for compatible TV programs and which are
previously stored before being exhibited. going to be transmitted around the current time, providing a
The ZapTV [4] developed for DVB-H standard allows the list of these programs. This list is also stored in a data base
user to create his own content, offering aggregated value with the date and time of generation.
services as multimodal access (Web and Cell phones), return The recommendations are presented to the user and those
channel, video note, personalized sharing and distribution of required are stored with the user history. During the time the
content. Besides the technology provided by DVB-H, ZapTV IDTV for cell phone is turned on, all programs viewed by the
comprehends other technologies as TV-Anytime, user are stored in the database which has the user history. This
Technologies emerging from Web 2.0 and involved in the process is repeated every time the user turns on the TV.
Semantic Web. The main functionalities of ZapTV include a Ginga-NCL middleware has a layer for resident applications
social net, personalized content broadcasting (implicit or responsible for exhibition, other layer for the common center
explicit recommendation), thematic channels diffusion responsible for offering several services and a last layer
planning (age-group, genre or specific theme), client regarding the protocols stack.
application and transmission of the electronic programming The recommendation system is considered as an element in
guide. ZapTV seeks to improve the recommendation using an Ginga-NCL architecture, in Ginga Common Core, due to the
intelligent personalization mechanism which matches need to continue using data locally and also use Tunner
information filtering with semantic logic processes and it was libraries – in order to obtain information about the channels
based on the principles of participation and sharing between tune – ESInformation – in order to obtain information about
Web 2.0 users, so that the creation, sharing, classification and EIT table generating the EPG – and Context Manager – to
note of content make the search for content easier. obtain system information.
Our recommendation system is in the portable device, and As GINGA-NCL middleware is mandatory in Brazil for
the inclusion of servers in Brazilian IDTV architecture for cell portable devices, the recommendation system was planned,

3. designed and modeled according to Brazilian rules which refer standard ABNT NBR 15603-3:2007, Annex C, "gender
to portable devices, thus meeting these devices needs. More descriptor in the descriptor content." [17]
details on our system can be obtained in. [15] A new table was created, identical to the EPG table, but
VI. TESTS with added fields with the names of genre, to be used with the
technique of the cosine. These fields were populated with 0 or
For the tests we used the data corresponding to TV viewing 1 depending on the program or not fit in that genre, becoming
and program schedule. These data were provided by IBOPE a matrix.
which is a Brazilian multinational private equity firms and a
leading market research in Latin America. 67 years ago to D. User History
IBOPE provides a wide range of information and media Historical data viewing of users are needed for the
studies, public opinion, voting intention, consumption, brand discovery of these preferences. In the context of digital TV
and market behavior. In the following subsections the that we are considering, these data are collected and stored
characteristics of these data and the tests will be detailed. [16] implicitly.
Spreadsheets sent by tuning IBOPE, which contains user
A. Characteristics of Residences data, were modified so that filtering techniques based on
Data that contain information provided IBOPE EPG (TV content could be applied.
programming), history of the user's view (what the viewer saw) The data were then separated by households and although
and also the socioeconomic information. All these data were some homes have more than one TV in these households it
separated and stored in MySQL database the data correspond was noticed that there is no record of monitoring more than
to 15 days of programming and monitoring of six Brazilian one TV at the same time and therefore it is considered that the
households with TV programming Open. These households household has only one TV.
were monitored every minute, and each individual was also Data were also formatted: date in yyyy-mm-dd, time in
monitored separately. hh:mm:ss format TV and 00X. The resulting sheets were
converted to CSV files that were then inserted in MySQL
TABLE I
NUMBER OF INDIVIDUALS BY RESIDENCE
were also added to user data, information for day of week,
time of day and duration of display.
Residence 1 2 3 4 5 6
Individuals 2 3 3 2 2 3 E. Methodology
TVs 1 1 2 2 1 2 We simulate the Content Filtering Based on using two
B. Characteristics of Date different techniques, the Apriori and cosine, both using as a
The data used for these tests have undergone a process of target attribute to gender. In the case of Apriori, we apply the
manual adjustment. For each of the algorithms used, was a settings shown in Figure 1.
necessary pre-processing for correct use and analysis. Then start the simulation for Apriori. For each household,
Subsections C, D, E and F detail the composition of these data. the process was the same on the first day to generate
recommendations for the second day, the second day, based
C. EPG on what was seen the day before and the present day, to
The EPG (Electronic Program Guide) is composed of 15 generate recommendations for the third day, and so on.
TXT files called programming files, one for each day First we opened the CSV file corresponding to the X home
(05/03/2008 to 19/03/2008) with a grid of 10 TV stations and day 1. After convert some attributes String to Numeric
Open, starting at 00:00:00 and ends at 05:59:00. After Nominal and others for Nominal (applying filters). Then the
understanding the files that make up the EPG, the data were executable Apriori and ultimately save the output. With the
copied from the archives of programming a spreadsheet data saved, it was possible to assess whether the day after,
BrOffice and then was done cleaning up unnecessary data. someone from the household attended any of the genera found
We noticed some inconsistencies in schedules, which were by Apriori.
immediately corrected so that future analysis will not generate To find the cosine first and save the profile, then calculate
erroneous results. This was repeated for each of the 15 the distance of the cosine, cosine and the standard itself and
programming files, generating a single spreadsheet containing finally found the right answers. The process is iterative and is
the entire 15 days of EPG. performed for each day and each household.
Was added to these data the day of week and duration of the
program. The EPG, this step is not complete, missing the F. Apriori e Cosine
genre and subgenre of each program. Searched for it on the The algorithms of association techniques identify
official websites of the gender of each station broadcast associations between data records that are somehow related.
programs and then identified according to the Brazilian The basic premise is evidence on the presence of others in the
same transaction, to determine what things are related.

4. Moreover, we also calculated the average percentage of
correct answers for the number of recommendations generated
using the following formula:
(2)
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. Figure 2. Parameters used in Cosine.
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). [18]
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
Figure 3. Parameters used in Apriori.
history. The similarity is high when the value of cosine is high,
the closer to 1, the greater the similarity. [19]
VII. RESULTS
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:
(1)
Figure 4. Comparison between means of Apriori and Cosine.

5. VIII. CONCLUSION ACKNOWLEDGMENT
During the tests, we observed some peculiarities. Our We thank IBOPE for providing real data about the
system recommends content based on the kinds of programs, electronic program guide and also the viewer’s behavior data
and our analysis were made according to that parameter. With from March, 05, 2008 to March, 19, 2008.
the Apriori algorithm, the format of the data is already
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