RECOMMENDER TV

A Personalized TV Guide System Compliant with Ginga

Paulo Muniz de Ávila

Department of Computer Science, Pontifical Catholic University of Minas Gerais

Av. Padre Francis Cletus Cox, 1661 Poços de Caldas, Brazil

Sérgio Donizetti Zorzo

Department of Computer Science, Federal University of São Carlos

Rodovia Washington Luís, km 235 - SP-310 São Carlos, Brazil

Keywords: Personalization, Multimedia, Profiling, Recommendation System, Digital TV, Middleware Ginga.

Abstract: With the advent of digital television and the possibility of transmission of new services (in the analogue

system, channels) a lot of information will be released in comparison to traditional analog system. The

electronic programming guide (EPG) responsible for organizing such information has become inefficient

because of the large volume of data provided by service providers. So that viewers can dealing with this

information overload, are necessary tools to identify their needs and preferences. Personalized

recommendation systems emerge as a solution to that problem, providing the viewer programs relevant to

your profile. In this paper we present a personalized recommendation system, the RecommenderTV

consistent with the reference implementation of the middleware Ginga. The implementation of the system

RecommenderTV demanded the inclusion of new features to the middleware Ginga-NCL none exists in the

implementation of reference. The service providers (in the analog system, broadcasters) and its importance

to the system RecommenderTV are discussed in this work. Finally, we are reported the results obtained

from the experiments with the system of recommendation implemented.

1 INTRODUCTION

An essential change has been occurring in TV

nowadays in Brazil: the migration from the

analogical system to the system digital TV. This

change has two main implications: the increase in

the transmission of new channels 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 information overload is unavoidable

making the EPG system inappropriate. In Shangai

(Zhang et. al, 2005), a big city in China, the TV

operators provide different services (in the

analogical system, channels), and this number has

been increasing at a rate of 20% per year. This way,

the traditional EPG system became unattractive

because it takes too long for the viewers to search in

the hundreds of options available to find their

favourite program. In face of this situation, the

personalized recommendation systems are

necessary.

Different from the 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

searching in many EPG options to find the favourite

program. The TV viewer’s profile can be realized in

an explicit way where the system receives

information about the preferences or it can be

realized in an implicit way where the system can

infer the TV viewers’ preferences analyzing their

behaviour background. In the DTV context, the

implicit option is surely the best in face of the

limitations imposed by the remote control to the data

149

Muniz de Ávila P. and Donizetti Zorzo S. (2009).

RECOMMENDER TV - A Personalized TV Guide System Compliant with Ginga.

In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 149-156

DOI: 10.5220/0002234301490156

 SciTePress

income. However, both systems can be used. To

make the benefits (new channels, interactive

applications) offered by the digital system possible,

the TV viewers with analogical system need a new

equipment called set-top box. Set-top box 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

set-top box needs a software layer which connects

the hardware to the interactive applications called

middleware. The DTV Brazilian System middleware

is Ginga (Soares et. al, 2007; Souza et. al, 2007).

This paper proposes an extension to Ginga-NCL

middleware through implementation of a new

module incorporated to the Ginga Common Core

called Recommender. The Recommender module is

responsible for gather, store, process and

recommend TV programs to the TV viewer. To

develop the Recommender module, it was used the

Ginga-NCL middleware developed by PUC-RIO

(Pontifical Catholic University of Rio de Janeiro),

implemented in C/C++ language with source code

available under GPLv2 license and according with

the patterns defined by the Brazilian system digital

television (“Ginga-NCL Virtual” , 2009).

The rest of this paper is organized as follow:

section 2 presents related works, section 3 describes

the providers, section 4 presents a general view of

Ginga-NCL middleware and the extensions

proposed to support the recommendation system;

section 5 presents details of implementation and

connection of new modules to Ginga-NCL

middleware; section 6 details the experiences, the

simulation environment and the results and section 7

presents the conclusion.

2 RELATED WORKS

In the last years, many TV personalized systems

have been developed to help the viewers in face of

the increasing offer of new services. The first

recommendation systems used explicit approach to

researches have been done aiming to infer

automatically the viewer’s preferences.

TV-Advisor (Xu et. al, 2002) uses explicit

techniques to create recommendation, demanding

the viewer to specify the interests to the

recommendation system.

PTV (Cotter and Smyth, 2000) is a system which

provides personalized recommendation to the viewer

based on the collaborative filtering approach. The

viewer’s preferences are recorded in an explicit way.

The Multi-Agent TV Recommender (Kurupati et.

al, 2001) matches both explicit and implicit methods

to store the viewer’s preferences and uses

collaborative filtering in the recommendation of TV

programs.

In (Ardisson et. al, 2001) a multi-agent

architecture for an adaptable EPG system is

presented. The viewer’s preferences depend on the

day and time he/she watches TV. The viewer’s

profile is generated using explicit and implicit

techniques.

AIMED (Hsu et. al, 2007) proposes a

recommendation mechanism that considers

tendencies like: mood, demographic information,

etc, to recommend programs. The approach used is

the hybrid approach based on the content filtering

methods and the collaborative filtering.

The Recommender TV system uses implicit

techniques to have the viewer’s profile. Implicit

techniques demand monitoring and analysis of the

viewer’s behavior background to have the profile.

3 SERVICE PROVIDER

This section presents important concepts related to

the service provider, how the digital sign

transmission is done and what information is

provided and the relation with the recommendation

system proposed in this paper.

Besides the transmission of audio and video, the

Brazilian system digital TV is supposed to send data

to the TV viewer. The service providers can send via

broadcast application written in Java

known as

Xlets or NCL applications, and both of them are

defined in the television Brazilian system. Besides

the application, the providers send tables which

transport information to the set-top box. This section

gives details about two important tables to this

context, the EIT (Event Information Table) and the

SDT (Service description Table).

Open digital TV systems adopt the pattern

MPEG – 2 System – Transport Stream (ISO/IEC

13818-1,2008) to the elementary stream

multiplexing. To understand what the elementary

streams are, it is necessary to understand how the

digital sign construction is done. In the first place

the audio captured by the microphone and the video

captured by the camera are sent separately to the

audio codifier and to the video codifier. The stream

of bits codifiers created separately is called

elementary stream. Once they are multiplexed in an

only stream of bits, the elementary stream is entitled

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transport stream. Two kinds of data structures can be

multiplexed in a transport stream: the Packetized

Elementary Stream-PES and the sections. The

sections are structures defined to transport the tables

that are not known as PSI-Program Specific

Information. The ABNT NBR 15603 (ABNT, 2009)

specifies in details the structure to build the basic

information related to the PSI which are part of the

Brazilian system of terrestrial digital television.

For the recommendation system proposed in this

paper, the SDT table transports the name of the

broadcasting station and the name of the service.

The Brazilian system digital TV allows a

broadcasting station to transmit more than one

service (in the analogical system, known as channel)

while the EIT table is responsible for the

transportation of the name of the program, start time,

duration and complementary information in its

descriptors. For example, the descriptor of extended

events of the EIT table allows the service provider

(broadcasting station) to specify a summary of the

program. These tables together transport essential

information to present the EPG and they are very

important in our recommendation system.

4 SYSTEM OVERVIEW

The recommendation system proposed in this paper

is based on Ginga middleware where the procedural

applications are developed using JavaTM language

and declarative applications in NCL. As mentioned

before, the version used was the open source version

of Ginga-NCL middleware.

Figure 1: Ginga Middleware Architecture (adjusted with

the recommendation system).

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, data stability. It is the architecture

core; stack protocol layer responsible for supporting

many communication protocols like HTTP, RTP and

TS. 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 Agent

Local, Agent Schedule, Agent Filter and Agent

Data. The second part describes the components

added to the set-top box: Sqlite (SQLITE, 2009), a C

library which implements an attached SQL database

and Weka (Waikato Environment for Knowledge

Analysis) (WEKA, 2009), open source code which

provides a set of algorithms to learn about the

machine and the data mining Figure 2 presents the

Recommender module architecture.

Figure 2: Recommender Module Architecture.

4.1 Implemented Modules

This subsection describes the modules added to the

Ginga middleware source code and the extensions

implemented to provide a better connection between

middleware and the recommendation system.

Agent Local is the module responsible for

constant monitoring of the remote control. Any

interaction between the viewer and the control is

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detected and stored in the database. The Agent Local

is essential for the recommendation system that uses

implicit approach to realize the profile.

Agent Scheduler 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. Agent Scheduler

module guarantees a new processing every 24 hours

preferably at night, when the set-top box is in

standby.

Agent Mining uses the algorithm package

provided by Weka to realize the data mining. Agent

Mining module accesses the information in the

viewer’s behavior background and the programming

data from the EIT and SDT tables stored in cache to

realize the data mining.

Agent Filter & Agent Data The raw data

returned by the Agent Mining module need to be

filtered and later stored in the viewer’s database. The

Agent Filter and Agent Data modules are

responsible for this function. The Agent Filter

module receives the data from the mining provided

by the Agent Mining 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 Agent Data module

receives the recommendations and stores them in the

viewer’s database.

4.2 Data Mining Algorithms

This subsection gives details about algorithms used

in data mining in Weka package. After the analysis

of some mining techniques, association standards

were adopted.

These are used to discover hidden patterns in

data sets which contain items related to the

occurrence of other items (Han and Kamber, 2006).

Among the existent algorithms based in this

technique, the Apriori is used in this paper. This

algorithm looks for affinity among items and it is

expressed in the form of standards, for example,

“70% of the visualization time on Mondays between

7 and 8 p.m. It is new”.

This technique was chosen due to its usage

background in many projects and to the analyzed

results since many behavior patterns were

discovered.

5 IMPLEMENTATION

This section presents implementation details of the

recommendation system. The great challenge in the

implementation of this system was extend the

Ginga-NCL middleware functionalities avoiding

alterations in the original source code. The loose

coupling between Ginga-NCL middleware classes

and the extension codes proposed in this paper is

essential to keep the compatibility with future

middleware versions. The extensions realized in

Ginga-NCL middleware source code are presented

below.

5.1 Extensions in Ginga

The Ginga-NCL middleware source code is

composed by many classes written in C/C++

language and distributed among 5 main modules:

ginga-cpp, gingancl-cpp, ncl30, telemidia-link-cpp

and telemidia-util-cpp. In order to implement the

recommendation system, the functionality of

gingacc-cpp and gingancl-cpp modules was

extended.

The gingacc-cpp module is responsible for

managing the reception of the transport stream, the

demultiplexing and the decodification. The

decodification process has three stages:

identification of the type of table, extraction of the

table and the storage in a volatile memory. Ginga

middleware does not implement mechanism to store

in cache the EIT and SDT tables transmitted by

service providers. To include this functionality, a

new class was implemented. This new class can

manage the storage in cache. From new

implemented services the Ginga-NCL middleware

started to provide such essential services for this

recommendation system.

The following components were implemented

and included as a Ginga Common Core extension:

Agent Scheduler module manages the Agent Mining

module and guarantees data mining every 24 hours

preferably at night, when the set-top box is in

standby. Agent Scheduler module is implemented in

C/C++ language. To have access to the Weka

package data mining functions written in Java

language, it was necessary to use JNI (Java Native

Interface), a set of APIs (Application Program

Interface) which allows the communication between

C/C++ language and Java

language. Agent

Scheduler module was implemented as a Thread

daemon.

With the purpose of saving unnecessary

processing resources, Agent Scheduler remains in

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sleep mode and check in regular periods of time the

set-top box intern clock to decide the right moment

to request the mining algorithm execution to the

Agent Mining.

Agent Scheduler module was developed to solve

a developing problem detected in data mining phase.

Data mining demands a lot of processing and time

and its execution is not practicable every time the

TV viewer requests a recommendation. In order to

solve this performance problem, RecommenderTV

system executes the mining every 24 hours and

stores the outcome in the viewer’s database. When

the TV viewer requests a recommendation, the

RecommenderTV check set-top box internal clock to

get the time and the date and then search in the

database what programs are recommended

considering the current date and time. This

approaching guarantees the efficacy of the system

because all recommendations are stored in the

database in advance. In previous versions of

RecommenderTV system, data mining was

processed every time the TV viewer requested a

recommendation. This approaching revealed itself as

extremely ineffective.

Agent Filter module waits Agent Mining execute

its algorithm. As soon as Agent Mining concludes

the data mining process, a message is sent to Agent

Filter module which performs a parser on the raw

data generated by the mining process. When the

processing is done Agent Filter request agent Data

module services to insert in the database the

program recommendation which will be available to

the TV viewer in the next 24 hours.

5.2 Modules Integrated in Ginga

In order to fulfill some necessities of the

recommendation system, two modules were

integrated to Ginga-NCL middleware.

This subsection describes the Sqlite database and

the Weka data mining package integration.

Sqlite is a library which implements a relational

database and it is written in C language. Due to the

fact that it is written in C and it provides the entire

source code, its integration with Ginga-NCL

middleware written in C/C++ language was easier.

In order to perform this integration, it was

necessary to link the Sqlite libraries with the

middleware libraries.

The Sqlite database was chosen due to three

facilities: 1) it is written in C language; 2) it was

projected to operate in attached devices; 3) it allows

Weka mining module to access the information

stored in the viewer’s database.

The Weka mining package is composed by a set

of algorithms which implement different techniques

of data mining. Its integration with Ginga-NCL

middleware was divided in two very different stages.

The first stage defines how Ginga-NCL middleware

written in C/C++ and Weka written in Java

communication is realized. The second stage gives

details on how the Weka module communication

was implemented with the database that provides the

TV viewer’s behavior background and the access to

EIT and SDT tables proceeding from the providers.

Weka is written in Java

language and its

integration with Ginga-NCL middleware written in

C/C++ needed a bridge to allow the communication

between the different languages. To make possible

the integration between Weka and Ginga, it was

necessary to use the resources provided by API

Java

Native Interface. API allowed Agent

Scheduler module to access Apriori mining

algorithm provided by Weka. This algorithm looks

for affinity among the items and expresses it through

standards like “70% of visualization time on

Mondays around 7 and 8 p.m. It is news”.

In order to the mining algorithms implemented

by Weka can produce program recommendations, it

is necessary two sets of well planned data: the TV

viewer’s behavior background and the program

grade available to the TV viewers by the providers

through EIT and SDT tables. The details on how

Weka module accesses this information are

described below.

Table 1: Viewer group visualization background.

Name category

of the

program

Day Time Min

P1 News Monday Night 8

P2 News Tuesday Night 20

P3 Kids Tuesday Morning 40

P2 News Tuesday Night 25

P4 Kids Tuesday Morning 40

The TV viewer’s behavior background is built by

collecting and storing any interaction between the

viewer and the set-top box. Table 1 presents a

sample of the behavior background information

stored in database used to show the outcomes

presented in this paper.

The data used to evaluate the RecommenderTV

system were collected during a period of 5 months in

a four-member family.

In order to process the data mining, the Mining

module has direct access to the database and

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recovers the TV viewer’s behavior background.

From the point of view of the system performance,

this communication between mining module and

Sqlite 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 TV

Recommender system. Agent Mining implements a

service which performs the data in cache reading

and converts at the same time to the format

interpreted and processed by Weka package.

6 EXPERIENCES

6.1 Results

This subsection describes the results after a month of

interaction with the recommendation system. In

order to measure the evolution of the

recommendation offered to the TV viewer, the

following formula was applied:

Ef=(α/β)100 (1)

Where EF is the efficacy of the recommendation

system, ranging from 0 to 100,

α is the

recommendation number accepted by the TV

viewers and β is the number of recommendation

presented. In order to monitor these indicators, two

control variables of the viewer’s data base were

preserved. β variable is increased every time the TV

viewer requests a recommendation,

α variable is

only increased when the TV viewer accept some of

the suggestions offered by the recommendation

system. Thus, the efficacy of the system can be

calculated daily, only verifying the variables values

and calculating the efficacy of the system.

During five weeks, a totally random

recommendation system was implemented in Ginga-

NCL middleware. The purpose of this random

system is to verify which TV viewer’s behavior

requests a recommendation and randomly receives a

recommendation. By this analysis it is possible to

verify if the viewer who requests a recommendation

will refuse it supposing any program of interest is

recommended. Figure 3 shows the random system

results.

Figure 3: Random System.

The efficacy of the random system outcome was

about 20% during the evaluation period. This data

shows that the TV viewer refuses any

recommendation that is not compatible with the

profile. The data to compose the chart were taken

out of the viewer’s database and to calculate the

system efficacy, the formula presented in (1) was

used.

Figure 4: Recommender System.

Figure 4 shows the recommendation evolution in

five weeks using the RecommenderTV system. In

the end of each week, the data with information

about the number of recommendation requested and

accepted were taken out of the viewer’s database

and applied in formula (1). The little efficacy of the

recommendation system in the first week is

explained when the data base is analyzed with the

TV viewer interactions with the set-top box. Little

information was available to the data mining

algorithms. On the second week, an evolution in the

recommendation system was noted. As the TV

viewer interaction with the set-top box was stored,

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the amount of information to the mining algorithm

increased and there was an improvement in the

quality of recommendation. On the third week, there

was not an increase in the efficacy as meaningful as

it was on the first and second week. The fourth and

fifth week presented a stability of the system,

keeping a success efficacy of about 80%.

Figure 5: Comparison between Recommender TV &

Random System.

The superiority of a system which uses data

mining mechanisms in comparison with random

systems and does not use any method to recommend

programs is evident. This superiority can be proved

in Figure 5 chart. Only in the first week, when the

RecommenderTV system had little information, the

efficacy percentages were close.

Figure 6: Application RecommenderTV.

Figure 6 shows RecommenderTV application.

The application used as front-end is written in NCL

and allows the TV viewer to search the

recommendation list selecting the wanted program.

7 CONCLUSIONS

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 viewers looking for their favorite

programs. These new mechanisms suggesting the

viewers programs are known as recommendation

systems.

A recommendation system compatible with

Ginga-NC middleware is presented in this paper and

it is implemented according to the standards of the

digital television Brazilian system.

All the extensions to Ginga middleware proposed

in this paper as the inclusion of new classes in the

Common Core, the Sqlite database linking and the

Weka mining module aim the increasing of the

efficacy in the recommendation system and the

inclusion of new functionalities non-existent yet in

Ginga.

With the purpose of simulating the life cycle of

the digital television Brazilian system which starts in

the service provider making the audio, video and

data available and finishes in the viewer set-top box,

a simulation environment was implemented. This

environment is composed by three service providers

transmitting audio, video and data to Ginga-NCL

Virtual set-top box which is in accordance with the

Brazilian standards executing in a MiniPc. Despite

Brazil broadcasts digital sign for more than one year,

this is limited to audio and video; data and

consequently sections and tables, and it is not yet a

reality in the country. It was a problem in the

validation of the recommending system because it is

necessary to access two important tables: EIT and

SDT and these tables are not broadcasted by the

service providers in Brazil so far. In face of this

limitation, an EIT and SDT table generator was

implemented in accordance with the Brazilian

standards.

In order to validate the recommendation system,

it would be adequate to store EIT and SDT tables in

the set-top box in advance, but for a simulation

closer to a real situation such tables were sent in a

transport stream what allowed verifying data

demultiplexing and decoding as well as testing new

modules included in Ginga-NCL middleware in

order to store the data decoded in cache.

With the implementation, it was clear that

Without the alterations proposed in this paper, a

recommendation system implementation is

impracticable. The necessity to keep the viewer’s

behavior information in data base associated with

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155

the necessity of storing the information coming from

the service providers require the linking of new

modules to Ginga middleware and the extension of

others. This paper described the complete

implementation of a recommendation system

compatible with Ginga middleware. The expectation

for future research is to extend the functionalities

implemented in RecommenderTV system, allowing

the interoperability with other devices through

UPnP

/DLNA (Forno et. al, 2006) protocol in a

home networks.

ACKNOWLEDGEMENTS

We thank the support of FINEP (the Brazilian

Federal Agency for Fostering Innovation) for

supporting the Avaliação do Middleware Ginga

Project (# 01.07.0110.00).

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