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The Burden of Choice
The Burden of Choice examines how recommendations for products, media, news, romantic partners, and even cosmetic surgery operations are produced and experienced online. Fundamentally concerned with how the recommendation has come to serve as a form of control that frames a contemporary American as heteronormative, white, and well off, this book asserts that the industries that use these automated recommendations tend to ignore and obscure all other identities in the service of making the type of affluence they are selling appear commonplace. Focusing on the period from the mid-1990s to approximately 2010 (while this technology was still novel), Jonathan Cohn argues that automated recommendations and algorithms are far from natural, neutral, or benevolent. Instead, they shape and are shaped by changing conceptions of gender, sexuality, race, and class. With its cultural studies and humanities-driven methodologies focused on close readings, historical research, and qualitative analysis, The Burden of Choice models a promising avenue for the study of algorithms and culture.
E-Commerce and Web Technologies
This book constitutes the refereed proceedings of the 5th International Conference on Electronic Commerce and Web Technologies, EC-Web 2004, held in Zaragossa, Spain in August/September 2004. The 36 revised full papers presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on recommender systems, databases and EC applications, service-oriented e-commerce applications, electronic negotiation systems, security and trust in e-commerce techniques for b2b e-commerce, negotiation strategies and protocols, modeling of e-commerce applications, e-commerce intelligence, e-retailing and Website design, and digital rights management and EC strategies.
Providing Actionable Recommendations
Recommender systems (RS) are intended to assist consumers by making choices from a large scope of items. By recommending items with a high likelihood of suiting a consumer's needs or preferences, they are able to considerably mitigate the information overload problem at the user's side, thus increasing their trust in, satisfaction with, and loyalty to RS providers, such as online shops, internet music catalogs, and online DVD rental services. However, recommendations are prone to errors and often fail to address consumers' context specific needs. Explanations of the underlying reasons behind recommendations can allow users to handle algorithmic errors in recommendations and to better judge t...
Encounters with HCI Pioneers
The huge success of personal computing technologies has brought astonishing benefits to individuals, families, communities, businesses, and government, transforming human life, largely for the better. These democratizing transformations happened because a small group of researchers saw the opportunities to convert sophisticated computational tools into appealing personal devices offering valued services by way of easy-to-use interfaces. Along the way, there were challenges to their agenda of human-centered design by: (1) traditional computer scientists who were focused on computation rather than people-oriented services and (2) those who sought to build anthropomorphic agents or robots based...
Predicting movie ratings and recommender systems
A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.
Computational Trust Models and Machine Learning
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
Advances in Next Generation Services and Service Architectures
The book is intended to provide readers with a comprehensive reference for the most current developments in the field. It offers broad coverage of important topics with eighteen chapters covering both technology and applications written by international experts.
The End of Ownership
An argument for retaining the notion of personal property in the products we “buy” in the digital marketplace. If you buy a book at the bookstore, you own it. You can take it home, scribble in the margins, put in on the shelf, lend it to a friend, sell it at a garage sale. But is the same thing true for the ebooks or other digital goods you buy? Retailers and copyright holders argue that you don't own those purchases, you merely license them. That means your ebook vendor can delete the book from your device without warning or explanation—as Amazon deleted Orwell's 1984 from the Kindles of surprised readers several years ago. These readers thought they owned their copies of 1984. Until,...
Building Successful Online Communities
How insights from the social sciences, including social psychology and economics, can improve the design of online communities. Online communities are among the most popular destinations on the Internet, but not all online communities are equally successful. For every flourishing Facebook, there is a moribund Friendster—not to mention the scores of smaller social networking sites that never attracted enough members to be viable. This book offers lessons from theory and empirical research in the social sciences that can help improve the design of online communities. The authors draw on the literature in psychology, economics, and other social sciences, as well as their own research, translating general findings into useful design claims. They explain, for example, how to encourage information contributions based on the theory of public goods, and how to build members' commitment based on theories of interpersonal bond formation. For each design claim, they offer supporting evidence from theory, experiments, or observational studies.
Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents
X Table of Contents Table of Contents XI XII Table of Contents Table of Contents XIII XIV Table of Contents Table of Contents XV XVI Table of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): IDEAL 2000, LNCS 1983, pp. 3›8, 2000. Springer-Verlag Berlin Heidelberg 2000 4 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 5 6 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 7 0.6 1.5 0.4 1 0.2 0.5 0 0 10 100 1000 10000 10 100 1000 Mutual information (bits) Mutual information (bits) 8 J. Sinkkonen and S. Kaski 20 10 0 0.1 0.3 0.5 0.7 Mutual information (mbits) Analyses on the Generalised Lotto-Type Competitive Learning Andrew Luk St B&P Neural ...
