87,210 research outputs found
Service Quality and Customer Loyalty in a Post-Crisis Context. Prediction-Oriented Modeling to Enhance the Particular Importance of a Social and Sustainable Approach
Research into the influence of service quality on customer loyalty has typically focused on confirming isolated direct causal influences regarding particular dimensions of quality, usually undertaken in the context of positive, firm-customer relations. The present study extends analysis of these factors through a new lens. First, the study was undertaken in a market context following a crisis that has had far-reaching consequences for customers’ relational behaviors. We explore the case of the Spanish banking industry, a sector that accurately reflects these new relational conditions, including a rising demand for more socially responsible banking. Second, we propose a holistic model that combines the effects of four key factors associated with service quality (outcome, personnel, servicescape and social qualities). We also apply an innovative predictive methodological technique using partial least squares (PLS) and qualitative comparative analysis (QCA) that enables us not only to determine the direct causal effects among variables, but also to consider different scenarios in which to predict customer loyalty. The results highlight the role of outcome and social qualities. The novelty of the social qualities factor helps to underscore the importance of social, ethical and sustainable practices to customer loyalty, although personnel and servicescape qualities must also be present to improve the predictive capability of service quality on loyalty
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
Oceanographic Weather Maps: Using Oceanographic Models to Improve Seabed Mapping Planning and Acquisition
In a world of high precision sensors, one of the few remaining challenges in multibeam echosounding is that of refraction based uncertainty. A poor understanding of oceanographic variability can lead to inadequate sampling of the water mass and the uncertainties that result from this can dominate the uncertainty budget of even state-of-the-art echosounding systems. Though dramatic improvements have been made in sensor accuracies over the past few decades, survey accuracy and efficiency is still potentially limited by a poor understanding of the “underwater weather”. Advances in the sophistication of numerical oceanographic forecast modeling, combined with ever increasing computing power, allow for the timely operation and dissemination of oceanographic nowcast and forecast model systems on regional and global scales. These sources of information, when examined using sound speed uncertainty analysis techniques, have the potential to change the way hydrographers work by increasing our understanding of what to expect from the ocean and when to expect it. Sound speed analyses derived from ocean modeling system’s three-dimensional predictions could provide guidance for hydrographers during survey planning, acquisition and post-processing of hydrographic data. In this work, we examine techniques for processing and visualizing of predictions from global and regional operational oceanographic forecast models and climatological analyses from an ocean atlas to better understand how these data could best be put to use to in the field of hydrograph
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT)
enable the assessment of students on a specific subject, which can be useful
for several purposes (e.g., learning path customization, drop-out prediction).
However, the questions have to be assessed as well and, although it is possible
to estimate with IRT the characteristics of questions that have already been
answered by several students, this technique cannot be used on newly generated
questions. In this paper, we propose a framework to train and evaluate models
for estimating the difficulty and discrimination of newly created Multiple
Choice Questions by extracting meaningful features from the text of the
question and of the possible choices. We implement one model using this
framework and test it on a real-world dataset provided by CloudAcademy, showing
that it outperforms previously proposed models, reducing by 6.7% the RMSE for
difficulty estimation and by 10.8% the RMSE for discrimination estimation. We
also present the results of an ablation study performed to support our features
choice and to show the effects of different characteristics of the questions'
text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence
in Educatio
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