AGENT-BASED PERSONALIZATION IN DIGITAL TELEVISION. P.O. Box 553, Tampere, Finland, {samuli.niiranen, artur.lugmayr,

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1 AGENT-BASED PERSONALIZATION IN DIGITAL TELEVISION Samuli Niiranen 1, Artur Lugmayr 1 and Seppo Kalli 1 1 Laboratory of Signal Processing, Tampere University of Technology, P.O. Box 553, Tampere, Finland, {samuli.niiranen, artur.lugmayr, seppo.kalli}@tut.fi ABSTRACT Digital television (digi-tv) is an emerging technology providing new facilities for multimedia content delivery. Three parties are involved in the delivery chain: Broadcast Service Provider (BSP), Interaction Service Provider (ISP), and end-user equipment (e.g. Set-Top Box, STB). The BSP uses a broadcast network to stream A/V content and multiplexed added-value applications to end users who can communicate with the ISP through a feedback channel. Due to the overwhelming amount of available content from multiple broadcasters, the appliance of personalization techniques can guide the user in search of optimal service and content provision. Within the scope of this paper we present a Foundation for Intelligent Physical s (FIPA) conformant solution for Audio/Vision Entertainment Broadcasting (AVEB) to perform agent-based personalization in a digital television environment. We develop two use scenarios and describe a FIPA compliant, JADE (Java DEvelopment)-based personalization agent test constellation and its deployment on a simulated digi-tv platform. 1. INTRODUCTION Personalization in digi-tv involves the combined use of technology and user/content information to tailor broadcast TV services to match the client s needs. Intelligent software agents are an applicable technique for performing personalization in digi-tv. Our work consists of three major parts: presentation of personalization techniques, their application in a FIPA AVEB compliant framework, and two use scenarios of utilizing this framework for broadcast digi-tv service personalization including a description of a test agent constellation deployment on the JADE platform. The deployed constellation corresponds to a part of the framework used in one of the use scenarios. 2. PERSONALIZATION TECHNIQUES In a generic media service environment, providing a user interface, multimedia content and interaction capabilities for user of the service, there are two approaches to personalization: user-profile based personalization and inference-based personalization with two generic application areas. 2.1 Generic application areas Content personalization In content personalization we personalize typically multimedia content to match the user s preferences. The aim in content personalization can also be to increase, in case of advertising or promotional information, the acceptability or susceptibility of the content for a particular user or user group. For example, personalized advertising can provide substantial benefits for the advertiser in terms of commercial effectiveness User interface personalization User interface (UI) personalization can provide personalized use experiences for, e.g., vision impaired people through the enlargement of user interface components. Other examples of user interface personalization include providing simplified interfaces for young children or the elderly. 2.2 User profile-based personalization User information collection By collecting user information we want to develop and maintain a user profile describing a user s interests or other descriptors relevant to personalization. Explicit profiling: A set of explicit questions is provided for the user of the service to query his preferences. Implicit profiling: Interaction of the user with the service is observed usually transparently. Use of legacy data: Legacy user profile databases, user behavior history, or previously collected user behavior material is mined for obtaining data about its behavior User profile analysis The generated user profiles are used as an input for the user profile analysis techniques used to select the actual personalization for the service. Rule-based techniques: Here we define explicit (e.g. business rules) for personalization. In practice this means that we must define conditions in the user

2 profile which trigger personalization functions and what to do about them. Filtering techniques use algorithms to analyze user profile and content metadata and select the appropriate personalization for content and user interfaces. Simple filtering: A predefined classification to user (e.g. age) groups is used to determine what content with what UI is provided for the user. Content-based filtering: The available content objects (or metadata describing them) are analyzed to select content representing best the user s interests. Collaborative or community-based filtering: User opinions on a set of content objects is collected, either explicitly or implicitly, to generate similarminded peer or interest groups (e.g. the football watcher s group) for predicting a user s interest in certain content Provision of the personalized service taxonomy is presented in Figure 1 classifies agents as follows: Cooperative agents are communicating agents; they communicate with other cooperative agents and act accordingly. Proactive agents initiate actions without user interaction. Adaptive agents can learn from past situations and experience how to behave in certain situations. Personal agents, which combine capabilities from adaptive and proactive agents, act proactively and serve individual users. Collaborative agents, which combine capabilities from cooperative and proactive agents, are proactive and cooperate with other agents. Smart agents combine capabilities from all ISA agents. After the personalization features are selected for the user using rule-based methods or filtering the personalized content and user interfaces are made available to the user Inference-based personalization This method relies on learning the user s behavior by monitoring user interaction with the application providing the service in real-time. Inference engines use sophisticated statistical approaches to extract trends from user behavior. In practice, the engine monitors, for example, the click stream data of the user for personalizing the content or user interface of the service. 3. AGENT-BASED PERSONALIZATION IN DIGI- TV A broadcast digi-tv environment differs from a generic media environment requiring push and push/pull service schemes, real-time capabilities, agent mobility, different involved domains, additional messaging facilities in the broadcast channel, and other characteristics. We will next discuss the application of the personalization techniques described in section 2 in the digi-tv domain by applying an agent-based approach Intelligent software agents Software agents are agents operating in a software environment including operating systems, computer applications, databases and networks, and represent a computational entity which exhibit autonomy, rational behavior, communal interaction, and mobility [1]. Intelligent software agent is an agent that uses artificial intelligence in the pursuit of the goals of its clients [2] and according to one definition [3] intelligent software agents have some or all of the following capabilities: cooperation, reactivity and adaptability. The ISA Figure 1. ISA taxonomy [3] ISAs exhibiting many of these capabilities can be utilized for providing personalization in a TV broadcast environment as discussed next FIPA compliant agent platform FIPA promotes technologies and interoperability specifications that facilitate the end-to-end interworking of intelligent agent systems in modern commercial and industrial settings [4]. In practice, FIPA has defined a standard model for deploying, managing, and for the access control of agents. The central element is the Management System (AMS), which maintains the agent life cycle and a list of agents, uniquely identified by their agent identifiers. Each agent has to register in advance at the AMS, confirming that the platform is capable of performing this task. A Directory Facilitator (DF) provides a yellow page service. Each entry describes shortly the service, properties, and attributes of an agent that can be queried, enabling agents to search for other agents to achieve their goal. The Message Transport System (MTS) and the Communication Channel (ACC) allow the communication of agent messages on one platform, and an over a distributed service pool. The payloads of agent messages are encoded and/or compressed according to the Communication Language (ACL). Each agent platform firstly initializes and configures the AMS, DF, MTS, and the ACC. Multiple platforms

3 might exist, logically and physically distributed over various systems. Each agent platform has basis container for agent deployment, for performing the life cycle of each agent in a parallel way. The main or root container manages AMS, DF, and MTS/ACC agents. The FIPA standards have previously been used to define an agent-based TV receiving system [5] Applying FIPA specifications in digi-tv Figure 2 shows the broadcasting architecture as specified by FIPA s AVEB [4] consisting of three integral domains. The digi-tv equipment domain represents the standardized hardware available as a mass product; from the user domain basic digi-tv functionality can be accessed via an abstract interface provided by wrapper components. The latter domain provides agent-based technologies and performs the actual personalization tasks, as on two different access types: TV broadcast only (push), and on-demand services (push/pull). In a production system multiple user domains exist, whose agents can communicate with each other. The content provider domain - with one or more ISP(s) and BSP(s) - is the end-node of the feedback or broadcast channel, requiring other types of agents for information collection. Personalization in digi-tv is a two-folded problem, requiring techniques for push/pull and push content. Revising section 2, two major personalization scenarios are possible within a FIPA AVEB digi-tv architecture User profile-based content personalization This push/pull scenario involves the recording/recommendation generation of A/V stream services preferred by the end-user (Figure 3). It involves all the presented agent types: the IA collects implicitly or explicitly user preferences; the UPA manages the user profiles and applies rule-based or filtering techniques in user profile analysis; the CA matches content descriptors with user s preferences in collaboration with the UPA and the GA for storing and presenting personalized content; A/V content descriptors are maintained by the GA Inference-based personalization In this scenario functionality of services delivered to the end-user in a push scheme is personalized (Figure 3). Here the IA monitors user interaction with the service and, by using an inference engine, communicates back to the service the personalization determined in real-time. Digital TV AV Streams Digital Storage Medium Tuner Video-On- Demand Server Provider Type 1 Non MTS Digital Storage Medium Wrapper Tuner Wrapper Video-On- Demand Control Wrapper Guide 1 Users (Family) Interface Control AVLover MTS Content Provider Domain User Profile Guide 2 User Domain User Profile User Group Domain User Domain Provider Type 2 Figure 2. Reference model for the AVEB application [4]. Within this architecture four different agents can be deployed for personalization purposes: Interface s (IA) interpret user requests, ACL ( Communication Language) messages, and present information in a human readable form. User Profile s (UPA) maintain user profiles, provide user preferences, involve conditional access schemes and filtering functionality, and communicate with other user groups. Guide s (GA) advertise services, perform content queries, collaborate with the CA, present query results, and perform customization tasks over the feedback channel. Control s (CA) glue the IA, GA, and UPA to one entity, promote interactions with other agents, retrieve user interaction, query user preferences, control local hardware, and match content with the GA for a user. Figure 3. -based personalization in the FIPA AVEB framework. 4. USE SCENARIOS OF AGENT-BASED PERSONALIZATION IN DIGI-TV We will illustrate the application of the FIPA AVEB framework by reviewing two specific use scenarios. 4.1 News broadcast compilation Current news broadcasts are split into self-containing video strips. Each strip has different topics, such as politics, economy, sports, etc. The goal is to provide a solution that records news broadcasts during one day personalized to the end-user. Thus, the end user obtains a recorded news broadcast consisting of news strips of different broadcasters that are in his interest. In practice this means that we provide a questionnaire in the user application for selecting the appropriate news

4 strips types and use content-based filtering for compiling the personalized news broadcast. In this scenario the home system automatically stores selected news broadcast video strips for a user (Figure 4). We employ user profile-based personalization with explicit user preference collection and content-based filtering to compile the personalized news broadcast. The user is initially explicitly queried for the news he s interested in. User s preferences gathered by the IA are transferred to the UPA for updating the user profile. The CA evaluates incoming content descriptions from the GA and consults the UPA; if there are any matches with the user s preference, the CA stores the selected video strips from a news broadcast into the STB DSM (Digital Storage Media). Video strips are compiled during a time interval of one day. Figure 4. Compilation of news broadcasts 4.2 Heath Care TV Personal treatment services provide a possibility for the personal administration of the treatment of various health conditions. The most common examples are services related to the treatment of diabetes, asthma and hypertension. Applications, run on the STB, providing access to these services through the feedback channel can be multiplexed to the digi-tv broadcast A/V streams. As the users of a treatment related treatment services are a very diverse group there exists a need for providing a personalized look and feel for different users. For example, functionality providing simplified user interfaces for those who seldom use the more advanced functions of the application is an interesting personalization task. In practice this type of personalization could be based on monitoring user interaction with the application and using this information to hide UI components rarely used by the user. For this use scenario we employ real-time inference based personalization (Figure 5). The IA monitors user interaction from the application and, by using an inference engine, communicates back to the application with the UI personalization. Figure 5. Inference-based UI personalization in personal health care treatment services 5. TV-ANYTIME CONTENT DESCRIPTION METADATA GENERATION WITH JADE SOFTWARE AGENTS For validating our FIPA AVEB framework approach we deployed a sample agent constellation for TV-Anytime content description metadata maintenance using FIPA compliant JADE (Java DEvelopment) software agents on a simulated digi-tv platform. The constellation models a part (used in the 1st use scenario) of our FIPA AVEB personalization framework and includes a generator agent and a guide agent functioning in the content provider domain of the framework. In practice, the deployed guide agent maintains TV-Anytime A/V content descriptions (in collaboration with the generator agent) in XML markup syntax for the content provider A/V stream TV programs. 5.1 TV-Anytime A/V content description metadata The TV-Anytime metadata system allows the consumer to find, navigate and manage content from a variety of internal and external sources including, e.g., enhanced broadcast, interactive TV, Internet and local storage. Furthermore, it defines a standard way to describe consumer profiles including search preferences to facilitate automatic filtering and acquisition of content by agents on behalf of the consumer [6]. TV-Anytime content description metadata is general information about a piece of content, such as a TV program, that does not change regardless of how the content is published or broadcasted. It includes information such as the content s title, textual description, and genre. Typically, the content creator assigns content description metadata before publication [6]. TV-Anytime metadata is structured into selfcontained documents. Each document has a single toplevel element that encloses all other TV-Anytime metadata. In our scenario a TV-Anytime program information document with content description metadata contains structured metadata about TV programs broadcasted by one content provider including the title and genre of the program JADE JADE (Java DEvelopment Framework) ( is a software framework for the development of agent-based applications in

5 compliance with the FIPA specifications for interoperable and intelligent multi-agent systems. JADE can be considered an agent middle-ware that implements an agent platform and also a development framework. It deals with those aspects that are not peculiar of the agent internals and are independent of the applications, such as message transport, encoding and parsing, or agent lifecycle constellation As stated, in our experiments we utilized TV-Anytime content description metadata with JADE used as the Java software framework for the deployed agents of the constellation functioning as one part of a FIPA AVEB compliant personalization system. The purpose of the constellation is to model A/V content description maintenance of a FIPA AVEB compliant digi-tv system (Figure 6). The agent constellation consists of two agents: guide agent and generator agent. The generator agent is an additional agent outside the FIPA framework, whose purpose in our test constellation is the announcement of program schedule information of one content provider to the corresponding guide agent as requested by it. Where the generator agent utilizes broadcast proprietary formats for representing program schedule information, the guide agent transcodes its information into TV-Anytime content descriptors. In a real deployment, a control agent requests these metadata description schemes for further personalization on the enduser device. the TV-Anytime content description in XML markup for the available TV programs. This agent initializes the whole process of obtaining the content descriptions by querying the Generator for available A/V content metadata regularly. This message is encapsulated via the ACL message format and sent to the Generator. In Table 1 are listed the two TV-Anytime content description metadata program information attributes used in our agent constellation. Name Title Genre Definition A title of the program. A program can have multiple titles, e.g. in different languages. Defined as an MPEG-7 datatype, TitleType (See Sec in [7] for a detailed specification). A genre for the program. Defined as an MPEG-7 datatype, GenreType (See Sec of [7] for a detailed specification). Table 1. Program information attributes used in the agent constellation [6] Figure 7 shows the child elements of the TVAMain root level element of a TV-Anytime program information document XML Schema. TV-Anytime content description metadata is located in the child elements of tva:contentdescription. Figure 6. Deployed agent constellation The basic functionality of the constellation is as follows: Generator : The behavior or simulation of the program schedule of a digi-tv broadcast A/V content provider is implemented within the scope of this agent. It generates random sequences of information indicating the genres and titles of TV programs made available by the content provider through the broadcast A/V streams. If this agent retrieves a query for broadcasted content metadata it generates a random sequence of program information entries with genres and titles for each entry, which are encapsulated into an ACL message and sent to the query source agent, the Guide. Guide : The guide agent retrieves the information generated by the provider agent and generates Figure 7. TVAMain root level element and child elements 5.3 constellation deployment For our digi-tv simulation environment, and agent constellation deployment, we used the following system setup: Intel Pentium 500 with 256MB RAM and Windows 2000 Java J2SE SDK Version 1.3 JADE Environment Version 2.4 Our experiments were based on a PC based demonstration, where agents communicated via an Internet connection.

6 5.3.1 Implementation Each agent has to register himself firstly at a yellow page service, containing information about agent behavior, characteristics, and communication protocol. To initialize the communication process, the guide agent sends a request to the generator agent to obtain personalization information in form of metadata structures. The obtained information is transcoded to TV-Anytime s content description format. The entire most significant part was the modeling and implementation of the behavior of both agents: Generator : The behavior implementation minimizes to communicating a schedule of running TV programs (with genre and title metadata) to the guide agent upon receiving a request. A simple database request and filtering currently running TV programs results in a sophisticated solution: while (true) { retrieve initialization message verify agent request generate program schedule information build reply message send reply message } Guide : A more complex behavior, especially a transcoding algorithm between incoming program schedule-, and outgoing TV-Anytime compliant data structures (including their storage) was implemented by the guide agent. Storage and metadata representation was realized using simple files, and the JDOM XML API ( initiate communication with generator agent while (true) { receive QUERY message decode generator agent s answer transcode schedule to TV-Anytime build, verify, and store metadata tree confirm received message by ACL CONFIRM } Both agents were deployed by utilizing the JADE agent deployment tool. The first command sent from the guide agent is QUERY-REF, initializing the communication process. In case of failure, the generator agent responses with NOT UNDERSTOOD, otherwise he transmits an INFORM message to the guide agent Experimental results The sample constellation functioned as expected supporting our agent-based approach. Figure 8 shows the JADE ACL sniffer tool in action monitoring agent communication in our constellation. Figure 9 shows the Directory Facilitator (DF) entries for our two agents. DF provides service location and registration in FIPA compliant agent systems. 6. CONCLUSIONS Personalization is an interesting technique for providing customized use experiences for a wide range of users and user groups. Digitalization of TV systems has made it possible to conceive new, user need driven, methods for customizing broadcast content. An agent-based personalization solution based on the FIPA AEVB framework represents an interesting solution for providing personalization in digi-tv. Figure 8. JADE ACL sniffer tool Figure 9. DF Entries for guide and generator agents 6. REFERENCES [1] Intelligent s Group (IAG), Software s: A review, May [2] D.W. Croft, Intelligent Software s: Definitions and Application, October [3] Simon Case, Nader Azarmi, Marcus Thint, Takeshi Ohtami, Enhancing E-communities with -Based systems, IEEE Computer, July [4] FIPA, FIPA Audio-Visual Entertainment and Broadcasting Specification, August [5] Y. Kim, Y. Murasaki and M. Shibita, Design and Implementation of -based TV Receiving System html [6] TV-Anytime Forum, Specification Series: S-3 On: Metadata (Normative) V1.0, February [7] ISO/IEC CD , Information Technology - Multimedia content description interface - Part 5 Multimedia Description Schemes, October 2000.