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In this paper we present our framework, SenSee, designed to provide context-aware and personalized search that can be used to support multimedia applications in making content recommendations. Our primary demonstration application built upon the framework targets the growing digital television domain where we foresee an increasing need for user-adaptive functionality to counter the looming content...
Previous personalized DTV recommendation systems focus only on viewers’ historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information—AIMED. The AIMED data is fed into a neural network model to predict...
Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference...
Developing a content-aware coding scheme that delivers ensured QoE for end-users includes finding out which content information will be most needed; especially when wireless networks are used to send video information. Wireless in-home networks suffer from interference which creates variable throughput, which causes problems when sending video material from one device to another. Temporal and quality...
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