BIPoDi TVR: Brazilian Interactive Portable Digital TV Recommendation System

Elaine Cecília Gatto Universidade Federal de São Carlos Rodovia Washington Luís, Km 235 Caixa Postal 676, CEP 13565-905

Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil

elaine_gatto@dc.ufscar.br

Sergio Donizetti Zorzo Universidade Federal de São Carlos Rodovia Washington Luís, Km 235 Caixa Postal 676, CEP 13565-905

Tel.: 55-16-3351-8232 – São Carlos – SP – Brasil

zorzo@dc.ufscar.br

ABSTRACT

Using the Brazilian digital television system, the possibility of

offering new services and programs, and consequently more

available content, will make it difficult for the users to select their

favorite programs. The Recommendation Systems become a tool

to solve these difficulties and they are able to improve

interactivity between the user and the digital television filtering

information filtering and personalizing the content offer. This

paper describes a recommendation system for Brazilian

interactive portable digital television focused on the cell phone

which makes this functionality possible and creates TV program

recommendation according to user TV programs preferences

when using television in the cell phone.

Categories and Subject Descriptors

H.3.3 [Information Storage and Retrieval]: Information Search

and Retrieval – Selection Process , Information Filtering;

H.5.1 [Information Interfaces and Presentation]: Multimedia

Information Systems.

General Terms

Algorithms, Desgin.

Keywords

Middleware Ginga, Mobile TV, Multimedia, Personalization,

Profiling, Recommendation System.

1. INTRODUCTION New services, products, contents, channels and business models

have been created with the digital television. The Brazilian digital

television system [1, 2] allows permanent and portable reception,

high audio and video quality and interactivity, creating different

contents for permanent and portable interactive digital television

users. The interactive portable digital television shares in only one

device, internet, TV, cell phone, and the TV signs for these

devices are already available in many Brazilian cities. Nowadays,

some kind of interactivity for the portable digital television has

been already offered in some countries which have this service,

for example, voting in programs, shopping advertisement,

electronic programming guide, etc.

The electronic programming guide [3, 4, 5] helps the user to find

the TV program he wants to watch. However, the increase of

content in electronic programming guide is unavoidable with the

inclusion of new channels and, due to the great quantity of

information; the user starts to find difficulties in choosing

programs, resulting in waste of time. The electronic programming

guide, overloaded with information, does not meet the user

necessity, as it does not take their preferences in account, and the

lists presentation on the screen becomes boring because they are

long.

For the portable TV users, this situation is even more aggravating.

The presentation of long programming lists on a reduced screen

will bring even more difficulties. So, the interactive portable

digital television users focus on the current lack of the device

resources and do not want to waste their time selecting programs.

Different from using digital television in houses where it is

common to change channels frequently and navigate the

electronic programming guide, interactive portable digital

television takes considerable time and energy. [6, 7]

Table 1. Comparison between permanent and portable digital

television in Brazil

Permanent Portable

Set-top-box

TV sets with built-in

converter

PDAs cell phones, Mini-TVs,

Smartphone’s, Blackberries,

Receptors for automobiles

Many users One user

Screen bigger than 30 inches Screen bigger than 10 inches

Permanent place Anywhere

Longer viewing time Shorter viewing time

No Return Channel defined Return channel from the cell

net

Reference implementation of

the available middleware

Reference implementation of

the non-available middleware

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In Brazil, the quantity of cell phones is much bigger than the

quantity of TV sets, what can quickly stimulate the use of digital

television in this kind of device when theses cell phones with

digital TV become more accessible to the population. [8, 9]

The main advantage of the portable digital television is that the

user can use it in any place and at any time. On the other hand, the

advantage of permanent digital television is watching the

programs at home for a longer time. Table 1 shows a comparison

between the permanent and portable digital television in Brazil.

The users of these devices need private attention due to the

current characteristics of this environment like processing power,

storage capacity and battery.

In order to enjoy all the potential provided by the interactive

portable digital television, a software is necessary to link the

hardware, the operational system and the digital television

interactive applications. Such software is the middleware called

Ginga in Brazil [10, 11]. Ginga middleware allows the

construction of declarative and procedural applications using

Ginga-NCL (Nested Context Language) [12] and Ginga-J (Java)

[13] respectively.

The proposed model in this work used a Ginga-NCL middleware

reference implementation. NCL [14] is a declarative language

used to authorize hypermedia documents and it was developed

based on a conceptual model which focuses on representing and

treating hypermedia documents. NCL is Ginga-NCL official

language and it can be used in portable devices.

Finally, the main goal of this work is to develop a

recommendation system for Brazilian interactive portable digital

television in order to recommend TV programs according to the

user profile.

This paper is divided in: section 1 presenting the context of the

work, section 2 presenting some correlated works, section 3

presenting the recommendation system for Brazilian interactive

portable digital television, as well as its characteristics,

architecture and implementation, section 4 presenting the results

and section 5 the conclusion.

2. CORRELATED WORKS There are many recommendation systems for set-top-boxes

allowing personalization services. More information about these

systems can be found in [15, 16, 17]. Developing recommendation

systems for cell phones with television is a current area of

research. Three works which applies recommendation techniques

for interactive portable digital television are presented bellow.

In [7] a recommendation system for the DVB-H (Digital Video

Broadcast – Handheld) standard [18] was developed according to

OMA-BCAST (Open Mobile Alliance-Mobile Broadcast Services

Enabler Suite) [19]. The authors have identified some

requirements for the recommendation systems dedicated to this

environment as scalability, response latency, flexibility for current

standards of transmission, user privacy protection, among others.

The recommendation system is in the category of systems with

filtering based on content using text mining.

It uses a simple interface with the user and accepts natural

language as text entry as well as four values reflecting the user

preferences for comedy, action, horror and eroticism. The

recommendation in this system occurs as follows: first, the texts

are extracted, next, the emotion in the text is analyzed and the

distances between the topics are calculated. For each entry, an

index is calculated and a list of programs organized by this index

is recaptured.

The ZapTV [20] 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 comprehends other

technologies as TV-Anytime [21], Technologies emerging from

Web 2.0 [22] and involved in the Semantic Web [23].

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.

The main purpose of the system is replacing the ordinary content

(Public Broadcasting Station) by a personalized and adjusted one

in order to provide more attractive content for the users. The

system architecture allows diffusing content both by broadcast,

like DVB-H, and by video streaming.

There is a server which locates the television flow and the data

service; and a content personalized server which is responsible for

attributing and managing personal content according to the user

preferences and viewing background as well as indicating when a

change from the ordinary content to a personalized content must

be performed.

The user section consists of portable devices which can perform

the client application and send back to the server the necessary

data helping to set their profile. On the client side there is the

Player module which, among other tasks, must execute the

contents according to the type of reception available in the device

and there is also a module to store the user data collection and

personalized content received from the server.

There is a module called control which is responsible for

performing the player when the user starts the applicative,

monitoring, capturing and preparing the user interactions to be

sent to the server among other tasks. The last module on the client

side is responsible for receiving the personalized content and

sending the captured data.

The Decissor module, on the server side controls the user profiles

in the data bank module, updates the user profile whenever it

receives information from the user about the behavior and selects

advertisements which have to be sent to the users according to

their profiles. The Web Server lodges the web services to manage

the system and the contents; and advertisements companies and

content providers can add, delete and modify contents, programs

and users.

There is also a module to control the data flow between the server

and the user and other module to the data bank which store the

profiles, the data collected from the user behavior and the contents

sent by providers. The last module on the server side is

Picture 1. Context of the system use

.

responsible for formatting the data providing a safe and adequate

communication among the modules.

Concluding, the system requires login/password and when the

user accesses the application for the first time, he fills in a form

with his preferences in order to generate his profile. After logging

in, the user starts watching television either by streaming or by

broadcast.

Both works aforementioned provide solutions to the

personalization and the information overload in digital television

in portable devices.

In [7] the recommendation system mechanism applies two

techniques: the text mining and filtering based in content besides

requiring some data from the; while in [20], the mechanism is

more sophisticated, using hybrid information filtering, semantic

logic and explicit and implicit user identification. The login is

necessary in all of them and the differentials of [24] are the

personalized advertisement and the reception of content either by

streaming or by broadcast.

The work proposed in this article uses a data mining algorithm

and implicit collection of the user behavior, which does not

require login/password from the user, and was particularly

developed for the Brazilian digital television system. However, its

model can be applied in other standards.

The recommendation systems from previous work are out of the

portable device, and this is the most noticeable difference of

model proposed in this work. Both systems include, inside

existing digital television architecture, its own architecture, like

content servers and electronic program guide servers.

In this work, the recommendation system is in the portable device

and the inclusion of servers in Brazilian interactive portable

digital television is not necessary for providing recommendation

and, therefore, there is no need of remote communication,

avoiding the user to pay by data traffic in the net to receive the

recommendation or send data, protecting the user data privacy.

3. BIPODI TVR The system proposed in this work aims at making easier the

interactive portable digital television user routine by interacting

through a simple interface which allows the user to watch his

favorite content without spending too much time to find it.

BIPoDi TVR (Brazlian Interactive Portable Digital TV

Recommender) was projected in order to be executed locally in

the cell phone with the digital television functionality. It is also

necessary that the device has Ginga-NCL middleware. Picture 1

shows the context to use BIPoDi TVR. The fixed and mobile

receptors receive audio, video and data and the middleware is

responsible for separating them.

The device must be able to receive the digital television

transmission with the help of an internal or external antenna

compatible with the standard transmission adopted in Brazil. The

user interacts with the television in the cell phone and all the

channels viewed during the period of use are stored.

The initial propose of BIPoDi TVR considers using the categories

and the TV programs start time. As soon as the user turns on the

TV in the cell phone, TV programs of his preference with time

close to current time are recommended.

BIPoDi TVR was developed using Ginga-NCL middleware. The

tests and the implementation were performed in Ginga-NCL

middleware for set-top-box because the implementation for this

middleware portable device is not available at the moment.

The processing starts when the user turns on the TV in his cell

phone. The user viewing background data collected until that

moment are mined in order to find the user profile.The data

resulting from the mining are formatted and the user profile is

stored in a data bank, together with date and time of generation.

Once the user profile is updated, he can look in the electronic

program guide for compatible TV programs with transmission

time close to the current time, generating a list with these

programs.

The list is cleaned and formatted and only the data related to date,

time, duration and broadcast station remains generating a new list

of programs. The list with the programs includes the

recommendations which are also stored in a data base with the

date and time of generation.

The recommendations are presented to the user and those which

are required are stored with the viewing background. All the

programs the user watched during the period the TV is turned on

in the cell phone are stored in the viewing background.

All the programs the user watched during the period the TV is

turned on in the cell phone are stored in the data base which

contains the viewing background. This process is repeated

whenever the user turns the TV on.

3.1 Implementation Ginga middleware has a layer for the resident applications

responsible for exhibition, other layer for the common core,

responsible for offering many services, and a last layer pertinent

to the pile protocols. BIPoDi TVR was implemented as an

element in Ginga architecture, in the common core layer (Ginga

Common Core), as illustrated in Picture 2.

BIPoDi TVR is divided in many modules and it was carefully

thought, designed and modeled particularly to portable devices,

considering its current characteristics in order to meet the

requirements of this environment and to agree with the Brazilian

rules for portable digital television.

The BIPoDi TVR Trigger is responsible for starting and finishing

the data processing of the system. The BIPoDi TVR Capture is

responsible for capturing and storing all the programs watched

during the period the TV is turned on in the cell phone, as well as

the information concerning to the programs like date, time,

channel and genre.

The BIPoDi TVR Mining is responsible for storing the user

profile. This module should also find, in the electronic program

guide the programs which can be recommended to the user

according to the profile generating results with complete

information. The BIPoDi TVR Filter is responsible for filtering

the relevant information resulting from the Mining module,

formatting them and creating a list of recommendation.

The BIPoDi TVR Presentation is responsible for presenting

recommendation as well as managing the time the

recommendation will be on the screen. The last module, BIPoDi

TVR Data Manager, is responsible for deleting the data as soon as

they became old.

BIPoDi TVR architecture has also data bases (files) to store the

user viewing background, the electronic program guide, the user

profile and the recommendations. Picture 3 shows the

recommendation system architecture.

3.2 Mining Algorithm The BIPoDi TVR Mining module uses a mining algorithm.

Among the several existent data mining methods and considering

the domain specificities of this application, it was possible to

verify that the bottom-up method in which the exploring process

tries to discover something that is not known yet by extracting

only the data standards, as well as the indirect or non supervised

knowledge search method and the association tasks are the most

adequate for this work. There are several algorithms which could

be tested. However, the purpose of this work is not studying,

testing and analyzing deeply and systematically the impact of data

mining techniques application on devices like cell phones.

The association techniques algorithms identify associations

among data registers related in some way. The basic purpose finds

elements involving the presence of others in a same transaction

with the aim at establishing what is related. The association rules

interconnect items trying to show characteristics and tendencies.

Association discoveries should point common and not so common

associations.

Apriori algorithm is frequently used for mining association rules

and can work with a high number of attributes creating many

combinations among them and successively searching all data

base, keeping an excellent performance relating the processing

time.

The algorithm tries to find all the relevant association rules among

the items which have the X (prior) ==> Y (consequent) shape. If

x% of the transaction containing X also contains Y, so x%

represents the confidence factor (confidence force of the rule).

The support factor corresponds to x% of times that X and Y occur

simultaneously on the total of registers (frequency). [25]

In order to prove that this algorithm meets the necessary

requirements of this work, the tests were performed using data

from house 1 and Apriori algorithm of Weka software. Table 2

shows a sample of the rules created by the software. Rule 1

indicates that the Variety/Others describer had 21 occurrences in

Record broadcasting station in house 1.

Table 2. Sample of rules created by Weka

No Rules

1

domicilio=1 nomeEmissora=Record

descSubGenero=Outros 21 ==>

descGenero=Variedade 21 conf:(1)

2 descGenero=Jornalismo 9 ==> domicilio=3 29

conf:(1)

Picture 2. Recommendation system in Ginga

middleware architecture

.

Picture 3. Modules BIPoDi TVR

.

3.3 Tests In order to test the proposed and implemented system, particularly

the mining algorithm, it is necessary to have the user viewing data

and also the electronic program guide. This data was provided by

IBOPE [26] and was treated through an almost entirely manual

process in order to fit the standard format used in Brazilian digital

television system and also to be used in Weka mining data

software [27] for the tests.

The data corresponds to 15 days of programming and monitoring

of 6 Brazilian houses. The electronic program guide is composed

of 15 TXT files called programming files, one for each day (from

March 3 2008 to March 19 2008) with 10 public broadcasting

stations starting at 00:00:00 and finishing at 05:59:00 a.m. Picture

4 shows a sample of initial layout of these files and Table 3 shows

how this layout was organized.

With the first line from Picture 4 as an example, it is possible to

identify field according to Table 4. After understanding the files

composing the electronic program guide, the data was copied

from the programming files to a BrOffice spreadsheet with paste

special resource. This resource allowed the data to be exported

exactly as it was built in the layout, separating the fields in

columns.

After exporting, the unnecessary data was discarded. At the

moment of exporting, the numeric data lost its format and then it

was reformatted according to Table 3. For convenience, the day

column was converted from text format to data format.

Table 3. TXT files layout

Description Type Initial Position

Broadcasting Station

Code Numeric (03) 1

Program Code Numeric (06) 24

Name of the Program Character (30) 30

Start of the Program Numeric (06) 160

End of the Program Numeric (06) 166

Table 4. Identifying the fields in TXT files

Column Content Identification

1 005100PNRE

XXXXX

005 Broadcasting

Station code

100PNREX

XXXX Discarded

2 002645RELI

GIOSO MAT

002645 Program Code

RELIGIOS

O MAT

Name of the

Program

3 000000 Discarded

4 0000 Discarded

5 06000008000

0DIA_05

060000 Start of the

Program

080000 End of the

Program

DIA_0

5

Day of the

Program

6

11111110000

00000000000

03XX

Discarded

Then, some contradictions about the time were noticed and

immediately corrected so as the future analyses do not provide

wrong results. This entire process was repeated for each of the 15

programming files, creating only one spread sheet with all the

electronic programming guide of this 15-day period.

The user behavior is composed of many spreadsheets called

tuning spreadsheet which has much more information than the

electronic programming guide. The tuning spreadsheets and the

electronic program guide have codes which identify the Public

broadcasting stations. There was the necessity of standardizing

these codes because the identification number was registered in a

different way in these files.

In order to avoid data contradictions, a Broadcasting Station

column was added in the electronic program guide and later the

Public broadcasting stations codes were standardized due to the

code conflicts among Bandeirantes, Record, Rede TV! and TV

Cultura broadcasting stations.

The day of the week and the duration of the program were also

added. The electronic program guide is not concluded yet, there is

still missing the genre and subgenre of each program. Therefore,

the transmitted programs genre was searched in official sites of

each broadcasting station and next was identified according to the

ABNT NBR 15603-2:2007 Brazilian standard, attachment C,

“Genre describer in the content describer” [28].

In order to make this identification easier, the filtering resource

was used to classify the electronic program guide according to the

name of the program. If the program was reprised within the 15-

day period, it would not be necessary to search again in the

broadcasting station website.

It is important to highlight that the electronic program guide

spreadsheet totalized about 4,500 lines, what corresponds to 4,500

registers in a data bank and identified about 800 different

programs. Picture 5 shows the program/category quantity relation

found in the electronic program guide.

Picture 4. Sample of the TXT files initial layout

.

Picture 6. Tuning spreadsheet sample

.

The data format sent by IBOPE can be seen in Picture 6 which

shows users behavior from house 2. The spreadsheet starts at

00:00:00 and finishes at 05:59:00 a.m. and the channel code is

recorded when the user watches the program.

Despite the fact that there are 3 individuals and only 1 TV in

house 2, IBOPE has collected the channels each person watched

individually providing information about the behavior of each

person in the house. Picture 7 shows the characteristics of the

house.

In order to work accordingly with the data, the tuning spreadsheet

was also modified. Each person had to be separated with theirs

respective channels, day, time house and TV. Date and time

columns were also formulated according to the standard used in

the Brazilian system. The same happened to all the spreadsheet

contents, creating a relation which can be seen in Picture 8.

The spreadsheets were converted in CSV files (Comma-separated

values) to be inserted in MySQL data bank and also to be used in

Weka.

After, each CSV file was inserted in the data bank and the

unnecessary registers were discarded. Date and time columns

were also converted in only one column according to the standard

format (aaaa-mm-dd:hh:mm:ss).

The next step was finding in the electronic program guide the

programs correspondent to the viewings. In the proposed

recommendation system the user behavior is monitored but not

minute to minute, as it happens in IBOPE data, but when the user

changes the channel.

In order to attain this goal, data resulting from the mixture of the

electronic program guide and the user behavior generating the

viewing background, were treated again. Channel changes were

identified, the program permanence time was calculated, the

repeated registers and fields were deleted. Thus, the data was in

compliance with the tests performed.

4. RESULTS The tests with Weka Apriori algorithm confirmed that this can be

adopted in the system because it is adjustable to this propose

necessities. From the rules created by Apriori, recommendations

were simulated and it was possible to analyze if the user was

watching the recommendation simulated by these rules. The

following formula was used to calculate the accuracy:

in which a is the number of viewed recommendations, b is the

number of performed recommendations and is the efficiency of

the system.

The results found in Pictures 9 and 10 are noticeable and make it

clear that the tests were satisfactory during the period of

evaluation. Picture 9 shows the quantity of recommendations the

user viewed and requested in house 1 during 15days. The darkest

line represents the viewed recommendations and the lightest line

represents the requested recommendations. The average was of

three recommendation viewings and two recommendation

requests per day. Picture 10 shows the accuracy reaching an

average of 77% during 15-day period.

It was possible to note other characteristics also related to the user

in house 1 like the average of 30 minutes in front of the TV per

day, 14 programs of different sub genres. Record and Globo as the

most viewed station and Saturday as the day of the week in which

the user spent more time in front of the TV.

(1)

Picture 5. Program/category quantity relation

.

200

150

100

50

0

Min

iser

ies s

Ero

tic c

Soap

Oper

a

Rea

lity

Show

Show

M

ovie

e H

um

oro

us

Info

rmat

ion

n

Educa

tive

Sport

t R

affl

e, T

eles

ales

, P

rize

Pri

ze

Deb

ate,

Inte

rvie

w

TV

Ser

ires

Ser

ires

O

ther

s s In

fanti

le e

New

s

Var

iati

es s

Quan

tity

Category

Picture 7. Characteristics of the monitored houses

.

Qau

nti

ty

1 2 3 4 5 6 0

1

2

3

Houses

no people

no TVs

It was also possible to verify the size of the user background files.

The tests were iterative and cumulative, that is, data was collected

on the first day of mining. On the second day, more data mined

with the data from the first day was collected. It was verified that

the data did not take more space proportionate to the number of

mining days. Picture 11 shoes the size of the files created for the

15-day period in house 1.

5. CONCLUSION The reason of this work is the fact that digital television in cell

phones is showing evidence of fast growth around the world.

Furthermore, the possibility of watching TV anywhere and at any

time in portable devices points that the personalization becomes

important to solve some difficulties caused by overload of

information in the EPG and also the time the users spend looking

for programs they are interested in.

The proposed recommendation system was designed considering

current characteristics of portable devices and situations of using

television in the cell phone. This model can be adjustable to other

standards and also to new portable devices in the market.

Furthermore, there was a concerning of designing the system

according with the Brazilian rules determined to portable devices,

due particularly to current impracticability of developing the

integrated system with a middleware to portable digital television

so that in the future the implemented code can be portable with

minimum modification and updating.

As future work, the program classification and synopsis are

intended to be included as parameter to discover user preferences.

As for the synopsis, it could be possible to discover, for example,

favorite movie actors and then recommend movies with these

actors. Many other user preferences can be discovered through the

program synopsis and our work intends to explore these options.

6. ACKNOWLEDGMENT We thank to IBOPE for providing real data of the electronic

program guide and also the user behavior data from March 5 to

March 19 2008.

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Picture 8. Spreadsheet relation

.

Picture 9. Viewed and Required Recommendations

.

Rec

om

men

dat

ions

/ S

oli

cita

tion

s

Days

0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

s

r

6

4

2

Picture 10. System Accuracy

.

Days

60

40

20

0

80

100

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Acc

ura

cy

6

4

2

0

8

10

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Days

Kylo

Byte

s

12

Picture 11. Size of the viewing background files

.

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