A personalized TV guide system compliant with Ginga

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.

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.

Workshop on Multimedia …, 2003

We present an overview of ongoing research, system design and middleware that has been implemented on top of a relational DBMS to provide advanced personalized information services in an integrated digital TV and Internet environment. The middleware provides automatic selection of programs or parts of programs based on users' preferences. It is compatible to the TV-Anytime Forum specifications for user and program metadata providing extended capabilities for information retrieval using the Extended Boolean model. Adaptation of preferences based on users' viewing histories is also supported as well as the integration of domain specific ontologies to metadata standards. The metadata are managed robustly by a relational dbms allowing concurrency, remote access and modification. The system design and implementation emphasizes the use of standards (like TV-Anytime, MPEG-7, XML) for interfacing with the open outside world. Video is accessed based on its content and delivered in a variety of delivering channels i ncluding TVs, PCs, mobiles and PDAs.

2001

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--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 to customize and personalize the search for relevant information.

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.

2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004

Digital television is a reality providing new facilities for multimedia content delivery. Due to the large amount of available content from multiple broadcasters, the use of personalized recommendation techniques can help the user in the process of consuming A/V content. The viewer, who is actually a passive subject, will be changed into an active one, who will be able to organize the TV Program Guide of its interest whenever and however he wants, achieving a real "à la carte TV". Within the scope of this paper we present an end-to-end system able to offer A/V contents according to the TV viewer preferences. The system adapts its suggestions to the changes in viewer's behaviour across the time.

1998

The future success of the Web as an information resource relies on new technologies to provide a more targeted content delivery system. At the moment users are responsible for locating and selecting content. In the future user modelling and information filtering techniques will partially relieve the user of this burden as new personalised content delivery services come online. PTV is one such service, dedicated to providing users with personalised TV guides. In this paper we describe and evaluate the PTV system, paying particular attention to the techniques used for selecting content that matches the personal preferences of individual users.

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

2004

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.

Lecture Notes in Computer Science, 2003

This paper presents the recommendation techniques applied in Personal Program Guide (PPG), a system generating personalized Electronic Program Guides for digital TV. The PPG recommends TV programs by relying on the integration of heterogeneous user modeling techniques.