An architecture for life-long user modelling

In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling

Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t. accuracy, diversity, computational cost, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel model that leverages Gaussian process regression for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.Comment: 16 pages, 5 figure

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Mineral Royalties: Historical Uses and Justifications

Governments and private landowners have collected royalties on mineral resources for centuries. When comprehensive measures to account for the environmental externalities of mineral extraction are politically or practically unavailable, federal and state governments may consider adjusting royalty rates as an expedient way to account for these externalities and benefit society. One key policy question that has not received attention, however, is whether a royalty rate can and should be manipulated in this way, assuming statutory discretion to do so. This article fills that gap by evaluating the argument for increasing federal or state fossil fuel royalty rates through historical, theoretical, and practical lenses. To that end, this article in turn considers the meaning of royalties, the economic justifications for royalties, the legislative history of the implementation of federal royalties, and the considerations that private landowners have relied upon in setting royalties. This article concludes that it would be appropriate for governments to adjust mineral royalty rates to account for negative externalities not otherwise addressed by regulation or to otherwise promote public welfare. Such use of royalties is consistent with the historical record. Royalties have been used as pragmatic policy tools from almost their inception, and federal and state governments have often exercised their existing statutory discretion to adjust mineral royalty rates to promote public welfare

Knowledge management : a learning mix perspective

How can an organization define policy for managing its knowledge ? In this article, an integrative model is proposed : the Learning Mix. It consists of 4 interacting facets: Information Technology, Learning Structure, Knowledge Portfolio and Learning Identity.knowledge management; learning; learning model

Intranet and Knowledge Management: Putting the Cart Before the Horse?

This paper explores the use of intranet-technology to support knowledge intensive decision-making in a technical service delivery process of a major oilfield services company. Our findings show that creating, mobilizing, and exchanging knowledge through an intranet-technology based system delivers forms of benefits to both the organization and its clients, and understanding what organizational knowledge is to be managed and the process of managing it define the role of technology that enables knowledge management