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TL;DR: It is shown that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way and may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methodssuch as Q-learning.
3,233 citations
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TL;DR: Results from the analysis of this large number of SUS scores show that the SUS is a highly robust and versatile tool for usability professionals.
Abstract: This article presents nearly 10 year's worth of System Usability Scale (SUS) data collected on numerous products in all phases of the development lifecycle. The SUS, developed by Brooke (1996), reflected a strong need in the usability community for a tool that could quickly and easily collect a user's subjective rating of a product's usability. The data in this study indicate that the SUS fulfills that need. Results from the analysis of this large number of SUS scores show that the SUS is a highly robust and versatile tool for usability professionals. The article presents these results and discusses their implications, describes nontraditional uses of the SUS, explains a proposed modification to the SUS to provide an adjective rating that correlates with a given score, and provides details of what constitutes an acceptable SUS score.
3,192 citations
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15 Dec 2008TL;DR: This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders.
Abstract: A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.
3,149 citations
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24 Jul 1998TL;DR: Several improvements to Freund and Schapire’s AdaBoost boosting algorithm are described, particularly in a setting in which hypotheses may assign confidences to each of their predictions.
Abstract: We describe several improvements to Freund and Schapire‘s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.
2,900 citations
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TL;DR: The per-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery, can be increased dramatically under this assumption, and a form of multiuser diversity via packet relaying is exploited.
Abstract: The capacity of ad hoc wireless networks is constrained by the mutual interference of concurrent transmissions between nodes. We study a model of an ad hoc network where n nodes communicate in random source-destination pairs. These nodes are assumed to be mobile. We examine the per-session throughput for applications with loose delay constraints, such that the topology changes over the time-scale of packet delivery. Under this assumption, the per-user throughput can increase dramatically when nodes are mobile rather than fixed. This improvement can be achieved by exploiting a form of multiuser diversity via packet relaying.
2,736 citations
Authors
Showing all 1881 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yoshua Bengio | 202 | 1033 | 420313 |
Scott Shenker | 150 | 454 | 118017 |
Paul Shala Henry | 137 | 318 | 35971 |
Peter Stone | 130 | 1229 | 79713 |
Yann LeCun | 121 | 369 | 171211 |
Louis E. Brus | 113 | 347 | 63052 |
Jennifer Rexford | 102 | 394 | 45277 |
Andreas F. Molisch | 96 | 777 | 47530 |
Vern Paxson | 93 | 267 | 48382 |
Lorrie Faith Cranor | 92 | 326 | 28728 |
Ward Whitt | 89 | 424 | 29938 |
Lawrence R. Rabiner | 88 | 378 | 70445 |
Thomas E. Graedel | 86 | 348 | 27860 |
William W. Cohen | 85 | 384 | 31495 |
Michael K. Reiter | 84 | 380 | 30267 |