27,947 research outputs found
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Adversarial Training Towards Robust Multimedia Recommender System
With the prevalence of multimedia content on the Web, developing recommender
solutions that can effectively leverage the rich signal in multimedia data is
in urgent need. Owing to the success of deep neural networks in representation
learning, recent advance on multimedia recommendation has largely focused on
exploring deep learning methods to improve the recommendation accuracy. To
date, however, there has been little effort to investigate the robustness of
multimedia representation and its impact on the performance of multimedia
recommendation.
In this paper, we shed light on the robustness of multimedia recommender
system. Using the state-of-the-art recommendation framework and deep image
features, we demonstrate that the overall system is not robust, such that a
small (but purposeful) perturbation on the input image will severely decrease
the recommendation accuracy. This implies the possible weakness of multimedia
recommender system in predicting user preference, and more importantly, the
potential of improvement by enhancing its robustness. To this end, we propose a
novel solution named Adversarial Multimedia Recommendation (AMR), which can
lead to a more robust multimedia recommender model by using adversarial
learning. The idea is to train the model to defend an adversary, which adds
perturbations to the target image with the purpose of decreasing the model's
accuracy. We conduct experiments on two representative multimedia
recommendation tasks, namely, image recommendation and visually-aware product
recommendation. Extensive results verify the positive effect of adversarial
learning and demonstrate the effectiveness of our AMR method. Source codes are
available in https://github.com/duxy-me/AMR.Comment: TKD
Understanding user experience of mobile video: Framework, measurement, and optimization
Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study
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