Trust: The Social Virtues and the Creation of Prosperity

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Economic Control of Quality of Manufactured Product.

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.

/pdf/recommender-systems-with-social-regularization-3ofnmqyikx.pdf

Recommender systems with social regularization

Enclosed is a copy of the Board of Governors’ Semiannual Agenda of Regulations for the period August 1, 1980 through February 1, 1981. The Semiannual Agenda provides you with information on those regulatory matters that the Board now has under consideration or anticipates considering over the next six months, and is divided into three parts: (1) regulatory matters that the Board had considered during the previous six months on which final action has been taken; (2) regulatory matters that have been proposed for public comment and that require further Board consideration; and (3) regulatory matters that the Board may consider over the next six months.

Federal Reserve Bank of New Yorkの制定せる財務諸表様式について

When people make decisions, they usually rely on recommendations from friends and acquaintances Although collaborative filtering (CF), the most popular recommendation technique, utilizes similar neighbors to generate recommendations, it does not distinguish friends in a neighborhood from strangers who have similar tastes Because social networking Web sites now make it easy to gather social network information, a study about the use of social network information in making recommendations will probably produce productive results In this study, we developed a way to increase recommendation effectiveness by incorporating social network information into CF We collected data about users' preference ratings and their social network relationships from a social networking Web site Then, we evaluated CF performance with diverse neighbor groups combining groups of friends and nearest neighbors Our results indicated that more accurate prediction algorithms can be produced by incorporating social network information into CF

http://www.personal.kent.edu/~fliu3/ESWA4339.pdf

Use of social network information to enhance collaborative filtering performance

Sometimes to measure the performance of an information system, unusually long response times of database operation need to be identified. Compared with Lloyd’s (1995) statistical process control chart, we propose an outlier resistant robust locally weighted scatterplot smooth (loess) based control chart that identifies effectively all the out of control points of operation in Lloyd’s (1995) data. Our findings provide guidance to management in information system quality control and more generally to all control chart users in cases where outliers occur in the charted data.

A new control chart based on the loess smooth applied to information system quality performance

The mobile economy is growing rapidly and creating increasingly diversified digital products available to consumers. Due to the cognitive limitation of adopters, they often face the issue of information overload. In order to cope with such issues, adopters typically utilize other information to reduce their uncertainty before adoption. This study focuses on determinants of uncertainty reduction during the adoption process. We do not study why individuals adopt mobile apps, but rather we are studying what perceptions individuals have on the innovation characteristics and social factors (such as friends who use and like the app) and how those perceptions affect their level of search effort and uncertainty. Innovation diffusion theory, herding behavior theory, information overload theory, and the theory of informational cascades are employed in the development of our research model. Findings from this study provide significant insights for developers and management in the mobile app economy.

/pdf/a-study-of-the-effects-of-social-factors-and-innovation-2q8kjh8ycn.pdf

A Study of the Effects of Social Factors and Innovation Characteristics on Search Effort and Uncertainty in Mobile App Adoption

This study investigates how potential adopters of mobile applications utilize online review systems to inform their perceptions on the application’s technology characteristics and thus inform their eventual adoption decision. Informational cascades and herding behavior theories are combined with the Innovation Diffusion Model and the Theory of Planned Behavior (TPB) to develop a research model. The review characteristics of aggregate review valence, overall rating, and review volume are related to the perceived technology characteristics of relative advantage, compatibility, and complexity. These, in turn, use the TPB as a lens to tie it all to the behavioral intention to adopt the mobile application. An online survey yielded 448 responses for analysis. The results yield some important insights and raises new questions for future evaluation.

Exploring the Effects of Aggregate Review Characteristics on Mobile Application Adoption

Exploring the Effects of Aggregate Review Characteristics on Mobile Application Adoption Completed Research Paper

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