36,347 research outputs found
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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
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
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Ubiquitous healthcare profile management applying smart card technology
Nowadays, the patient-centric healthcare approach is focused on ubiquitous healthcare services. Furthermore, the adoption of cloud computing technology leads to more efficient ubiquitous healthcare systems. Moreover, the personalization of the delivery of ubiquitous healthcare services is enabled with the introduction of user profiles. In this paper, we propose five generic healthcare profile structures corresponding to the main categories of the participating entities included in a typical ubiquitous healthcare system in a cloud computing environment. In addition, we propose a profile management system incorporating smart card technology to increase its efficiency and the quality of the provided services of the ubiquitous healthcare system
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
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