23,172 research outputs found

    A component framework for personalized multimedia applications

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    Eine praktikable Unterstützung für eine dynamische Erstellung von personalisierten Multimedia-Präsentationen bieten bisher weder industrielle Lösungen noch Forschungsansätze. Mit dem Software-technischen Ansatz des MM4U-Frameworks („MultiMedia For You“) wird erstmals eine generische und zugleich praktikable Unterstützung für den dynamischen Erstellungsprozess bereitgestellt. Das Ziel des MM4U-Frameworks ist es den Anwendungsentwicklern eine umfangreiche und anwendungsunabhängige Unterstützung zur Erstellung von personalisierten Multimedia-Inhalten anzubieten und damit den Entwicklungsprozess solcher Anwendungen erheblich zu erleichtern. Um das Ziel eines Software-Frameworks zur generischen Unterstützung der Entwicklung von personalisierten Multimedia-Anwendungen zu erreichen, stellt sich die Frage nach einer geeigneten Software-technischen Unterstützung zur Entwicklung eines solchen Frameworks. Seit der Einführung von objektorientierten Frameworks, ist heute die Entwicklung immer noch aufwendig und schwierig. Um die Entwicklungsrisiken zu reduzieren, sind geeignete Vorgehensmodelle und Entwicklungsmethoden erstellt worden. Mit der Komponenten-Technologie sind auch so genannte Komponenten-Frameworks entstanden. Im Gegensatz zu objekt-orientierten Frameworks fehlt derzeit jedoch ein geeignetes Vorgehensmodell für Komponenten-Frameworks. Um den Entwicklungsprozess von Komponenten-Frameworks zu verbessern ist mit ProMoCF („Process Model for Component Frameworks“) ein neuartiger Ansatz entwickelt worden. Hierbei handelt es sich um ein leichtgewichtiges Vorgehensmodell und eine Entwicklungsmethodik für Komponenten-Frameworks. Das Vorgehensmodell wurde unter gegenseitigem Nutzen mit der Entwicklung des MM4U-Frameworks erstellt. Das MM4U-Framework stellt keine Neuerfindung der Adaption von Multimedia-Inhalten dar, sondern zielt auf die Vereinigung und Einbettung existierender Forschungsansätze und Lösungen im Umfeld der Multimedia-Personalisierung. Mit so einem Framework an der Hand können Anwendungsentwickler erstmals effizient und einfach eine dynamische Erstellung ihrer personalisierten Multimedia-Inhalte realisieren

    Hierarchical Attention Network for Visually-aware Food Recommendation

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    Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

    The contribution of data mining to information science

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    The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

    Affective Music Information Retrieval

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    Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio

    The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events

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    Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes

    Service Platform for Converged Interactive Broadband Broadcast and Cellular Wireless

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    A converged broadcast and telecommunication service platform is presented that is able to create, deliver, and manage interactive, multimedia content and services for consumption on three different terminal types. The motivations of service providers for designing converged interactive multimedia services, which are crafted for their individual requirements, are investigated. The overall design of the system is presented with particular emphasis placed on the operational features of each of the sub-systems, the flows of media and metadata through the sub-systems and the formats and protocols required for inter-communication between them. The key features of tools required for creating converged interactive multimedia content for a range of different end-user terminal types are examined. Finally possible enhancements to this system are discussed. This study is of particular interest to those organizations currently conducting trials and commercial launches of DVB-H services because it provides them with an insight of the various additional functions required in the service provisioning platforms to provide fully interactive services to a range of different mobile terminal types

    Deep Learning based Recommender System: A Survey and New Perspectives

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    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
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