Improving the quality of the personalized electronic program guide

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.

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.

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.

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.

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

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.

IEEE Transactions on Broadcasting, 2000

Due to the rapid increase of contents available under the convergence of broadcasting and Internet, efficient access to personally preferred contents has become an important issue. In this paper, an automatic recommendation scheme based on collaborative filtering is presented for intelligent personalization of (IP)TV services. The proposed scheme does not require TV viewers (users) to make explicit ratings on their watched TV program contents. Instead, it implicitly infers the users' interests on the watched TV program contents. For the recommendation of user preferred TV program contents, our proposed recommendation scheme first clusters TV users into similar groups based on their preferences on the content genres from the user's watching history of TV program contents. For the personalized recommendation of TV program contents to an active user, a candidate set of preferred TV program contents is obtained via collaborative filtering for the group to which the active user belongs. The candidate TV programs for recommendation are then ranked by a proposed novel ranking model. Finally, a set of topranked TV program contents is recommended to the active user. The experimental results show that the proposed TV program recommendation scheme yields about 77% of average precision accuracy and 0.135 value of (Average Normalized Modified Retrieval Rank) with top five recommendations for 1,509 people.

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.

2009

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

2019

In today's society due to the increase of the quantity of information is becoming more difficult to find the information we search. "Data mining" offers us the most important methods and techniques in data analysis. Through this work, we aim to study the several data mining techniques, methods and applications in specific areas. We experiment with an “open software" WEKA, to perform some data analysis, presenting the reliability and advantages of data mining classification technique. We use the decision trees technique to achieve the task of classification, to customize user profiles based on their requirements and needs. This paper presents also how machine learning methods can be integrated with agent technology in building more intelligent agents. Using machine learning techniques makes it possible to develop agents able to learn from and adapt to their environment. So a TV decoder can be adapted to the demands of TV viewers. If the decoder initially trained by...