193,646 research outputs found
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Realising Team-Working in the Field: An Agent-based Approach
Multi-agent systems technology is applied to enable co-operation between mobile workers in the field, minimising user intervention and increasing reachability. A component-based approach is taken to simplify the management of deployed co-operation services. A Personal Assistant running on a mobile device is introduced to show how an intelligent and autonomous agent can increase the utility of users during workforce co-operation processes. Finally, a real world trial of the technology by network installation and maintenance engineers in the UK is described. Some technical issues revealed during the trial are discussed, as is the impact of the technology on the business process
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Generic system architecture for context-aware, distributed recommendation
In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Supporting Device Discovery and Spontaneous Interaction with Spatial References
The RELATE interaction model is designed to support spontaneous interaction of mobile users with devices and services in their environment. The model is based on spatial references that capture the spatial relationship of a userās device with other co-located devices. Spatial references are obtained by relative position sensing and integrated in the mobile user interface to spatially visualize the arrangement of discovered devices, and to provide direct access for interaction across devices. In this paper we discuss two prototype systems demonstrating the utility of the model in collaborative and mobile settings, and present a study on usability of spatial list and map representations for device selection
Non-functional Property based service selection: A survey and classification of approaches
In recent years there has been much eļ¬ort dedicated to developing approaches for service selection based on non-functional properties. It is clear that much progress has been made, and by considering the individual approaches there is some overlap in functionality, but obviously also some divergence. In this paper we contribute a classiļ¬cation of approaches, that is, we deļ¬ne a number of criteria which allow to differentiate approaches. We use this classiļ¬cation to provide a comparison of existing approaches and in that sense provide a survey of the state of the art of the ļ¬eld. Finally we make some suggestions as to where the research in this area might be heading and which new challenges need to be addressed
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
In the vast and expanding ocean of digital content, users are hardly satisļ¬ed with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an eļ¬ective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named āVisionā. Within this recommender, selection criteria of candidate ļ¬elds and contextual factors are designed and usersā dependencies on their personal pref-erence or the aforementioned contextual inļ¬uences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, ļ¬nal experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be ļ¬exibly used for diļ¬erent recommendation purposes
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