A Contextual-Bandit Approach to Personalized News Article Recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.Comment: 10 pages, 5 figure

Tracking battery state-of-charge in a continuous use off-grid electricity system

The growing importance of batteries in the delivery of primary energy, for example in electric vehicles and isolated off-grid electricity systems, has added weight to the demand for simple and reliable measures of a battery’s remaining stored energy at any time. Many approaches to estimating this battery state-of-charge exist, ranging from those based on a full appreciation of the chemistry and physics of the storage and delivery mechanisms used, and requiring extensive data on which to base an estimate, to the naïve and simple, based only, for example, on the terminal voltage of the battery. None, however, is perfect, and able to deliver a simple percentage-full figure, as in a fuel gauge. The shortcomings are due to a range of complicating factors, including the impact of rate of charge, rate of discharge, battery aging, and temperature, to name just some of these. This paper presents a simple yet effective method for tracking state-of-charge in an off-grid electricity system, where batteries are in continuous use, preventing static parameter measurements, and where charge/discharge cycles do not necessarily follow an orderly sequence or pattern. A reliable indication of state-of-charge is, however, highly desirable, but need be only of fuel gauge precision, say to the nearest 12-20%. The algorithm described utilises knowledge of the past, and constantly adapts parameters such as charge efficiency and total charge capacity based on this knowledge, and on the occurrence of specific identifiable events such as zero or full charge

Quantifying, Modeling and Managing How People Interact with Visualizations on the Web

The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage people’s open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on people’s interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a user’s interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of people’s exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in people’s interactions with visualizations. 3. Create tools that manage and analyze an audience’s interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audience’s interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiences’ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web

Information technology and performance management for build-to-order supply chains

En las siguientes líneas se plantea un artículo de reflexión que tiene en cuenta parte del marco teórico que sustenta la investigación titulada “Prácticas pedagógicas que promueven la competencia argumentativa escrita (CAE) en niños campesinos de los grados 4° y 5° del Centro Educativo Municipal La Caldera, Sede Principal de Pasto”, desarrollada en el año 2012. En él se contemplan los aportes de las ciencias del lenguaje y la comunicación, la teoría de la argumentación, la didáctica de la lengua escrita y los géneros discursivos, que dan cuenta de la necesidad de desarrollar la capacidad crítica en los estudiantes a través de la argumentación, lo cual implica transformar las prácticas pedagógicas para que se alejen de la transmisión de conocimientos y den paso a la comunicación, para que la palabra escrita sea apropiada de manera significativa

Wearable cameras for real-time activity annotation

Google Glass has potential to be a real-time data capture and annotation tool. With professional sports as a use-case, we present a platform which helps a football coach capture and annotate interesting events using Google Glass. In our implementation, an interesting event is indicated by a predefined hand gesture or motion, and our platform can automatically detect these gestures in a video without training any classifier. Three event detectors are examined and our experiment shows that the detector with combined edgeness and color moment features gives the best detection performance

Assessment and learning outcomes: the evaluation of deep learning in an on-line course

Using an online learning environment, students from European countries collaborated and communicated to carry out problem based learning in occupational therapy. The effectiveness of this approach was evaluated by means of the final assessments and published learning outcomes. In particular, transcripts from peer-to-peer sessions of synchronous communication were analysed. The SOLO taxonomy was used and the development of deep learning was studied week by week. This allowed the quality of the course to be appraised and showed, to a certain extent, the impact of this online international course on the learning strategies of the students. Results indicate that deep learning can be supported by synchronous communication and online meetings between course participants.</p