Individual Preference Learning With Collaborative Learning Framework
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Individual Preference Learning With Collaborative Learning Framework
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Author : Zhu, Xi
language : en
Publisher:
Release Date : 2020
Individual Preference Learning With Collaborative Learning Framework written by Zhu, Xi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Smart, personal devices that interact with individuals make it possible to trigger desired behavioral changes with personalized incentives. Personalized incentives are the incentives that suit an individual's preferences. In this dissertation, individual preferences refer to a set of parameters describing how the individual values each influential factor in a travel alternative. To trigger behavioral changes with personalized incentives, a model that can accurately and efficiently estimate an individual's preferences from his behavior data is required. Two challenges exist in individual preference learning. For the first, the number of observations available from each individual for individual preference learning is limited. This issue causes difficulties in preference updating. For the second, the observability of the choices made is limited. This is because that it is not possible to directly observe the preference parameters -- the only information that can be observed is an individual's choice-making behavior. The two challenges prevent the use of traditional preference-learning techniques such as advanced econometric models (e.g., discrete choice models) derived from Random Utility Maximization (RUM).Other techniques such as machine learning also cannot be applied for similar reasons. New methods are needed for individual preference learning. This dissertation contributes to the existing literature in travel behavior studies by proposing individual preference learning methods such that personalized incentives could be accurately estimated to trigger behavioral changes, and proposing a design of an online experiment to collect travel behavior data. Specifically, two research questions are of interest: (1) What methodology could be used to learn an individual's preferences with only a few observations of choices made by him? (2) How to collect individuals' choice data to test the method proposed in the dissertation in terms of triggering individual behavioral changes with personalized incentives? In the dissertation, the behavior data is collected via a carefully designed online experiment utilizing the AMT (Amazon Mechanical Turk) platform. Considering the validity and reliability of the data, the dissertation contributes to the travel behavioral study in: (1) a full factorial design of a randomized experiment with two factors (commuting time and work flexibility, each with three levels) utilizing the online platform of AMT (Amazon Mechanical Turk) to collect individuals' travel choices on departure time in a sequence of hypothetical scenarios, and (2) a design of data quality control strategies, which refers to the design of some methods to reduce and identify the low-quality data collected in the experiment. These data quality control methods, such as understanding check, response consistency check, responding time record, and social desirability scale, can be applied to other online experiments and behavioral studies. To learn an individual's preference from a few choices made by him, a model structure that integrates a time-varying model and the collaborative learning model is proposed in the dissertation. The time-varying model is used to replace the original constant preference parameter to a time-dependent function, allowing an individual's preferences to fluctuate in his choice-making process. The collaborative learning model can exploit the underlying canonical structure of individuals' preference variation in a heterogeneous population. Specifically, the collaborative learning model could identify several patterns of preference changes (known as "canonical models") that exist in the population. With the canonical models, each individual's preference change can be expressed by a linear combination of all those canonical models. Considering the model's computation time, an online updating strategy for the proposed model is also proposed, such that individual preferences could be learned accurately and efficiently. Detailed specifications of two different formulations of the time-varying model are presented in the dissertation, with some explorations on model properties with simulations. The models are also applied to the real-world dataset collected in the online experiment. Results show that the proposed models can achieve higher accuracy in parameter learning and behavioral prediction than traditional preference learning models such as the logit model and the mixed logit model.
Preference Learning
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Author : Johannes Fürnkranz
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-19
Preference Learning written by Johannes Fürnkranz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-19 with Computers categories.
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Individual Preferences In E Learning
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Author : Howard Hills
language : en
Publisher: Routledge
Release Date : 2017-05-15
Individual Preferences In E Learning written by Howard Hills and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-15 with Business & Economics categories.
Trainers and educators ask: 'What personality types do best at e-learning; who really likes e-learning?' Better that they should ask: 'How can we make e-learning more appealing to more people?' E-learning is here to stay in the same way that the Internet is here to stay. The classroom, as a mass education tool, was an invention of the industrial age and we have made good use of it. E-learning is an invention of the information age but we have yet to properly realise its potential. Some of the steam has gone out of e-learning. Organizations have experienced problems with technology, variable content, poor course take-up and even greater drop-out. The problem is that what appeals to the organization, a mass training and development medium that can be used to train everyone at once, is at odds with - or at least ignorant of - the learning needs of the individual. Individual Preferences in e-Learning focuses on the process of e-learning, with the emphasis on learning and individual differences. With a firm rooting in previous research, in particular the author's in-depth knowledge of the MBTITM functions, this book shows you how to make e-learning work for different personality types.
Preference Learning
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Author : Johannes F Rnkranz
language : en
Publisher: Springer
Release Date : 2011-03-30
Preference Learning written by Johannes F Rnkranz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-30 with Machine learning categories.
A Short Introduction To Preferences
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Author : Francesca Bellet
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
A Short Introduction To Preferences written by Francesca Bellet and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice. Table of Contents: Introduction / Preference Modeling and Reasoning / Uncertainty in Preference Reasoning / Aggregating Preferences / Stable Marriage Problems
Advances In Social Networking Based Learning
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Author : Christos Troussas
language : en
Publisher: Springer Nature
Release Date : 2020-01-20
Advances In Social Networking Based Learning written by Christos Troussas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-20 with Technology & Engineering categories.
This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis. Although these three technologies have been widely used by researchers around the globe by academic disciplines and by R&D departments in the IT industry, they have not yet been used extensively for the purposes of education. The authors present a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content and learning resources based on the needs and preferences of individual learners. According to reports, in 2018 the vast majority of internet users worldwide are active on social networks, and the global average social network penetration rate as of 2018 is close to half the population. Employing social networking technologies in the field of education allows the latest technological advances to be used to create interactive educational environments where students can learn, collaborate with peers and communicate with tutors while benefiting from a social and pedagogical structure similar to a real class. The book first discusses in detail the current trend of social networking-based learning. It then provides a novel framework that moves further away from digital learning technologies while incorporating a wide range of recent advances to provide solutions to future challenges. This approach incorporates machine learning to the student-modeling component, which also uses conceptual frameworks and pedagogical theories in order to further promote individualization and adaptivity in e-learning environments. Moreover, it examines error diagnosis, misconceptions, tailored testing and collaboration between students are examined and proposes new approaches for these modules. Sentiment analysis is also incorporated into the general framework, supporting personalized learning by considering the user’s emotional state, and creating a user-friendly learning environment tailored to students’ needs. Support for students, in the form of motivation, completes the framework. This book helps researchers in the field of knowledge-based software engineering to build more sophisticated personalized educational software, while retaining a high level of adaptivity and user-friendliness within human–computer interactions. Furthermore, it is a valuable resource for educators and software developers designing and implementing intelligent tutoring systems and adaptive educational hypermedia systems.
A Conceptual Framework For Personalised Learning
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Author : Philipp Melzer
language : en
Publisher: Springer
Release Date : 2018-07-30
A Conceptual Framework For Personalised Learning written by Philipp Melzer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-30 with Computers categories.
Philipp Melzer analyses influence factors of personalised learning aiming to lay out design principles for personalised blended learning courses. Finding only weak support for a matching between learning styles and teaching methods,he defines learning tasks as the object of further investigations. Following the idea of a community of inquiry, the author develops the Personalised Learning Framework (PLF), modelling personalised learning as a process of selection as well as usage of learning tasks and learning tools by the community of inquiry. To evaluate the PLF further, a traditional university course is transformed to a personalised flipped classroom course. He shows how personalised learning can be supported in concrete learning interventions using specific learning methods and technologies.
Learning Style In A Personalized Collaborative Learning Framework
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Author : Nuzulla Mamat
language : en
Publisher:
Release Date : 2013
Learning Style In A Personalized Collaborative Learning Framework written by Nuzulla Mamat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Handbook Of Research On Collaborative Learning Using Concept Mapping
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Author : Lupion Torres, Patricia
language : en
Publisher: IGI Global
Release Date : 2009-07-31
Handbook Of Research On Collaborative Learning Using Concept Mapping written by Lupion Torres, Patricia and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.
This new encyclopedia discusses the extraordinary importance of internet technologies, with a particular focus on the Web.
Learning Preference Skills
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Author : Jennifer Barnes
language : en
Publisher: Hyperion Books
Release Date : 1992
Learning Preference Skills written by Jennifer Barnes and has been published by Hyperion Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Cognitive styles categories.