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Outline

Title
Abstract
Overview of the Personal Program Guide
Recommendation of TV Programs
The Evaluation Methodology
The Results
Conclusions and Future Work
Related Work
Related Work on TV Recommender Systems
Related Work on Interface Design
Related Work on Trust
Metadata
Interface Design
Recommendations, Feedback & Devices
PTV Case-Studies
Case Study 1 ^PTV on the Web
Case Study 2 ^PTV Mobile
Case Study 3 ^PTV Remote
Background
Similarity-Based Recommendation
Association Rule Mining for Recommendation
Experimental Results
Test Data
Experimental Setup
Recommendation Accuracy
Inter-Dataset Accuracy
Implications
Density Ðdatasetþ ¼ Number of Nonzero Entries
Recommendation Diversity
Recommendation Engine for Audio-Visual Stories
Do Subjects Follow a Clear Strategy?
Do Subjects Take Misery Into Account?
Do Subjects Follow the Rules?
Results and Discussion
Do Subjects Use Normalization?
Do Subjects Deduct Misery?
Do Subjects Use Linear Rating Scales?
Experimental Design
Research Questions
Subjects
In£uence of Topical Relatedness
Conclusions from the Study
Subjects Used in the Experiments
Method
Type of Data Used and Analysis Flow
Determination of Axes Related to Program Selection
Application of Results
Application to a TV Program Recommendation System
Discussion
Experiment Design
Results: Comparative Performance
Results: Comparative Satisfaction
Future Research
Related Work in Content Analysis and Enhanced TV
The Design Process
System Overview
Face Clip Finding
Summary Image Selection
Personalizing Web Data
Real-Time Analysis and Tagging
Conclusion and Future Work
Introduction
Iterative Design
Analysis
User Analysis
Results of the Brainstorming Session
Results of Interactive Design Session
Results
Results of the First Usability Test
Presentation of Recommendations
Providing Feedback on Recommendations
Explanations of Recommendations
Conclusions
Future Evaluations and Research
User Interface Design Process
Extended Architecture and Communication Protocol
Discussion and Related Work
Evaluation Study
Design
Procedure
The Questionnaires
Conclusion
References
All Topics
Computer Science

Personalized Digital Television

Profile image of Liliana ArdissonoLiliana Ardissono

2004

Abstract

This chapter presents the recommendation techniques applied in Personal Program Guide (PPG). This is a system generating personalized Electronic Program Guides for Digital TV. The PPGmanages a user model that stores the estimates of the individual user’s preferences for TV program categories. This model results from the integration of di¡erent preference acquisitionmodules that handle explicit user preferences, stereotypical information about TV viewers, and information about the user’s viewing behavior. The observation of the individual viewing behavior is particularly easy because the PPG runs on the set-top box and is deeply integrated with the TV playing and the video recording services o¡ered by that type of device.

Related papers

PersonalTVware: A Proposal of Architecture to Support the Context-aware Personalized Recommendation of TV Programs
Graça Bressan

soc.kuleuven.be

The arrival of Digital TV has ensued the growth in the volume of TV programs offered by TV operators increasing the difficulty by users in locating relevant content. In addition, TV users are not required to have as their main task to search for information as it is the case of the Internet. Within this scenario, the recommender systems stand out as a possible solution for this problem. However, the context has rarely been explored during the recommendation process. This paper presents a proposal of architecture of support to context-aware personalized recommendation for Digital TV-entitled PersonalTVware. Finally, a recommender process is described grounded on the proposed architecture.

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TV Personalization System
John Zimmerman

Human-Computer Interaction Series, 2004

The arrival of PVRs (Personal Video Recorders)-tape less devices that allow for easy navigation and storage of TV content-and the availability of hundreds of TV channels in US homes have made the task of finding something good to watch increasingly difficult. In order to ease this content selection overload problem, we pursued three related research themes. First, we developed a recommender engine that tracks users' TV-preferences and delivers accurate content recommendations. Second, we designed a user interface that allows easy navigation of selections and easily affords inputs required by the recommender engine. Third, we explored the importance of gaining users' trust in the recommender by automatically generating explanations for content recommendations. In evaluation with users, our smart interface came out on top beating TiVo's interface and TV Guide Magazine, in terms of usability, fun, and quick access to TV shows of interest. Further, our approach of combining multiple recommender ratings-resulting from various machine-learning methods-using neural networks has produced very accurate content recommendations.

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Personalisation technologies for the digital TV world
Barry Smyth

2000

Abstract. The information overload problem is becoming a serious issue on the Internet and users are finding it increasingly difficult to quickly locate the right information at the right time. Content personalisation techniques may provide a useful solution by enabling information services to respond to the implicit and explicit preferences of individual users.

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Collaborative Personalized Digital Interactive TV Basics
Jurij Tasič

With the introduction of interactive user oriented TV, digitalization and ipTV, a whole broadcast world is facing new challenges as well as new opportunities. The world of personalized digital interactive TV today is a fast developing area with well established and proven research concepts. Nowadays devices should be designed to learn for themselves and according to the accepted knowledge to prioritize the available programs for the final user. The emergence of the Digital TV technologies calls for investigating consumer preferences and behavioural models towards this new promising communication services. In the paper we are presenting the basic principles of a user based recognition and modelling of personalised interactive digital TV technologies which might increase and create new opportunities and home services.

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Chapter 5 TV PERSONALIZATION SYSTEM Design of a TV Show Recommender Engine and Interface
Anna Buczak

2008

The arrival of PVRs (Personal Video Recorders)-tape less devices that allow for easy navigation and storage of TV content-and the availability of hundreds of TV channels in US homes have made the task of finding something good to watch increasingly difficult. In order to ease this content selection overload problem, we pursued three related research themes. First, we developed a recommender engine that tracks users' TV-preferences and delivers accurate content recommendations. Second, we designed a user interface that allows easy navigation of selections and easily affords inputs required by the recommender engine. Third, we explored the importance of gaining users' trust in the recommender by automatically generating explanations for content recommendations. In evaluation with users, our smart interface came out on top beating TiVo's interface and TV Guide Magazine, in terms of usability, fun, and quick access to TV shows of interest. Further, our approach of combining multiple recommender ratings-resulting from various machine-learning methods-using neural networks has produced very accurate content recommendations.

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TV PERSONALIZATION SYSTEM Design of a TV Show Recommender Engine and Interface
John Zimmerman

The arrival of PVRs (Personal Video Recorders)-tape less devices that allow for easy navigation and storage of TV content-and the availability of hundreds of TV channels in US homes have made the task of finding something good to watch increasingly difficult. In order to ease this content selection overload problem, we pursued three related research themes. First, we developed a recommender engine that tracks users' TV-preferences and delivers accurate content recommendations. Second, we designed a user interface that allows easy navigation of selections and easily affords inputs required by the recommender engine. Third, we explored the importance of gaining users' trust in the recommender by automatically generating explanations for content recommendations. In evaluation with users, our smart interface came out on top beating TiVo's interface and TV Guide Magazine, in terms of usability, fun, and quick access to TV shows of interest. Further, our approach of combining multiple recommender ratings-resulting from various machine-learning methods-using neural networks has produced very accurate content recommendations.

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TV Scout: Guiding Users from Printed TV Program Guides to Personalized TV Recommendation
Patrick Baudisch

2002

In this paper, we present TV Scout, a recommendation system providing users with personalized TV schedules. The TV Scout architecture addresses the "cold-start" problem of information filtering systems, i.e. that filtering systems have to gather information about the user's interests before they can compute personalized recommendations. Traditionally, gathering this information i nvolves upfront user effort, resulting in a substantial entry barrier. TV Scout is designed to avoid this problem by presenting itself to new users not as a filtering system, but as a retrieval system where all user effort leads to an immediate result. While users are dealing with this retrieval functionality, the system continuously and unobtrusively gathers information about the user's interests from implicit feedback and gradually evolves into a filtering system. An analysis of log file data gathered with over 10,000 registered online users shows that over 85% of all first-time users logged in again, suggesting that the described architecture is successful in lowering the entry barrier. 1 1 The work presented in this paper was carried out during the authors' affiliation at GMD-IPSI

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Ptv: Intelligent personalised tv guides
Barry Smyth

2000

Abstract Although today's world offers us unprecedented access to greater and greater amounts of electronic information, we are faced with significant problems when it comes to finding the right information at the right time–this is the essence of the information overload problem. One of the proposed solutions to this problem is to develop technologies for automatically learning about the implicit and explicit preferences of individual users in order to customise and personalise the search for relevant information.

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TV recommendation and personalization systems: Integrating broadcast and video on-demand services
Paula Viana

2014

The expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs' characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

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Personal TVware: An Infrastructure to Support the Context-Aware Recommendation for Personalized Digital TV
Graça Bressan

International Journal of Computer Theory and Engineering, 2012

The coming of the Digital TV will bring a significant increase in the number TV programs offered by TV operators. Consequently, the user are facing it difficulty to find out the most interesting TV programs among the various options available. In this new scenario, the recommender systems stand out as a possible solution to the information overload problem. However, the current approaches to recommend content for Digital TV rarely considers the context during the recommendation process. Thus, this paper presents a software infrastructureentitled PersonalTVware-to support context-aware recommendation of TV programs. To validate the PersonalTVware, a context-aware recommender system was implemented as a concept proof. In order to evaluate the quality of the recommendation, a number of experiments have been conducted. The results indicate that consider both user's profile and context can provide better recommendations. Index Terms-Context-awareness, interactive digital TV, recommender systems, ubiquitous computing. applications. Luiz Gustavo Pacola Alves holds a M.Sc. in Electrical Engineering and B.Sc. in Computer Engineering from Polytechnic School of University of Sã o Paulo (EP-USP), Brazil. He has published academic papers on his main research areas which include Interactive Digital TV (metadata, middleware and applications), Context-Aware Computing and Collaborative Systems (CSCW). He worked as a researcher engineer at Laboratory of Computer Architecture and Network (LARC-USP) from 2002 to 2008. Since joining SAMSUNG Electronics in 2009 he has worked with broadcasters, consumer electronics manufacturers, software industries, universities and regulatory governmental agencies to establish technical standards for Brazilian Digital Television System, called SBTVD. Recently he has been focusing on collaboration on GINGA Test Suite specification, a collection of tests to drive the interoperability among the GINGA (SBTVD middleware based on NCL/Lua and Java technologies) implementers. Graç a Bressan got her PhD in Electronic Engineering (1986) at Polytechnic School of University of Sã o Paulo (EP-USP), Brazil. Nowadays, at the Computer Engineering Department at EP-USP, she teaches and develops research on Computer Network, Digital TV and Performance Analysis and has published articles and has oriented Msc and PhD degree students. Her current research interests includes Computer Networks and Digital TV focusing in the aspects of Distributed Systems, Distributed Middleware, QoS Mechanisms, Collaborative Virtual Environment, Middleware for Digital TV, Video-Conferencing, Modeling and Performance Analysis of Networks, and Application in Distance Education.

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