899 resultados para User interest model


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Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

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In recent years, evacuation models have been increasingly applied in an attempt to understand the outcome of emergency egress scenarios.

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This paper describes an end-user model for a domestic pervasive computing platform formed by regular home objects. The platform does not rely on pre-planned infrastructure; instead, it exploits objects that are already available in the home and exposes their joint sensing, actuating and computing capabilities to home automation applications. We advocate an incremental process of the platform formation and introduce tangible, object-like artifacts for representing important platform functions. One of those artifacts, the application pill, is a tiny object with a minimal user interface, used to carry the application, as well as to start and stop its execution and provide hints about its operational status. We also emphasize streamlining the user's interaction with the platform. The user engages any UI-capable object of his choice to configure applications, while applications issue notifications and alerts exploiting whichever available objects can be used for that purpose. Finally, the paper briefly describes an actual implementation of the presented end-user model. © (2010) by International Academy, Research, and Industry Association (IARIA).

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We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to iden- tify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conve- niently explore the PoIs. Motivated by this, we propose the length- constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of in- terest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP- hard to answer this query. We develop an approximation algorithm with a (5 + ǫ) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuris- tic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computingresults efficiently and effectively.

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Understanding the dynamics of interest rates and the term structure has important implications for issues as diverse as real economic activity, monetary policy, pricing of interest rate derivative securities and public debt financing. Our paper follows a longstanding tradition of using factor models of interest rates but proposes a semi-parametric procedure to model interest rates.

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