The Importance of Context When Recommending TV Content: Dataset and Algorithms

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions

Subjective Annotations for Vision-Based Attention Level Estimation

Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.Comment: 14th International Conference on Computer Vision Theory and Application

Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective

This paper studies context-aware recommendations in the television domain by proposing a deep learning-based method for learning joint context-content embeddings (JCCE). The method builds on recent developments within recommendations using latent representations and deep metric learning, in order to effectively represent contextual settings of viewing situations as well as available content in a shared latent space. This embedding space is used for exploring relevant content in various viewing settings by applying an N -pairs loss objective as well as a relaxed variant introduced in this paper. Experiments on two datasets confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Further experiments display useful structures in the learned embeddings that can be used to gain valuable knowledge of underlying variables in the relationship between contextual settings and content properties

AAL2 switching node to support voice services in 3rd and 4th generation networks

Includes bibliographical references.The research community and industry alike have, over the past decade, been showing considerable interest in packet-switching networks to support voice services as well as data services. A technology that was standardised to accommodate these delay-sensitive requirements is Asynchronous Transfer Mode (ATM), which deals particularly well at transporting uncompressed voice and data. However, due to the exponential increase in wireless applications and their supporting access technologies, a need has arisen for an infrastructure in the wide area network to support and maintain the QoS requirements of low-bit rate, compressed voice. An adaptation layer known as AAL2 was re-standardised to support these specialised voice services. However, a severe side-effect of using AAL2 with traditional ATM switches results in inefficient routing and waste-age of resources. In this study, a design for an AAL2 switching node will be proposed to address the above-mentioned issues. The design is comprised of modules that perform the following functions: Buffering, payload interrogation, protocol translations, packet classification, packet re- routing, timing, scheduling and support for signalling and management interfacing. The supporting architecture is targeted towards an embedded >286-based computing system, which itself is overlaid upon one or several ports of a high-speed, research-oriented ATM switch, known as the Washington University Gigabit Switch (WUGS). In order to evaluate the operation and performance of the AAL2 switch architecture, a testbed is proposed and implemented, comprising the AAL2 switch at the core, with a supporting infrastructure to emulate the generation and analysis of low bit-rate voice traffic over an AAL2 connection. By conducting a set of experiments, a series of operational and performance results will be presented. Particular focus will be placed on the performance and efficiency of the AAL2 layer over ATM, as well as the ability of the switch to route packets from multiple sources to a set of output connections in the correct manner