19,145 research outputs found
A methodology of personalized recommendation system on mobile device for digital television viewers
With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW.These annotations will be used to find other programs that are similar by using CBSA.Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually
A methodology of personalized recommendation system on mobile device for digital television viewers
With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized
recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism
of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW. These annotations will be used to find other programs that are similar by using CBSA. Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer
individually
Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
In the context of Social TV, the increasing popularity of first and second
screen users, interacting and posting content online, illustrates new business
opportunities and related technical challenges, in order to enrich user
experience on such environments. SAM (Socializing Around Media) project uses
Social Media-connected infrastructure to deal with the aforementioned
challenges, providing intelligent user context management models and mechanisms
capturing social patterns, to apply collaborative filtering techniques and
personalized recommendations towards this direction. This paper presents the
Context Management mechanism of SAM, running in a Social TV environment to
provide smart recommendations for first and second screen content. Work
presented is evaluated using real movie rating dataset found online, to
validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging
Technologies for Education. SETE 201
Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering
In this paper, we propose a new approach for recommender systems based on
target tracking by Kalman filtering. We assume that users and their seen
resources are vectors in the multidimensional space of the categories of the
resources. Knowing this space, we propose an algorithm based on a Kalman filter
to track users and to predict the best prediction of their future position in
the recommendation space
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NoTube – making TV a medium for personalized interaction
In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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