Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.

/pdf/a-survey-of-trust-and-reputation-systems-for-online-service-2vd0g0048y.pdf

A survey of trust and reputation systems for online service provision

Are trust and risk important in consumers' electronic commerce purchasing decisions? What are the antecedents of trust and risk in this context? How do trust and risk affect an Internet consumer's purchasing decision? To answer these questions, we i) develop a theoretical framework describing the trust-based decision-making process a consumer uses when making a purchase from a given site, ii) test the proposed model using a Structural Equation Modeling technique on Internet consumer purchasing behavior data collected via a Web survey, and iii) consider the implications of the model. The results of the study show that Internet consumers' trust and perceived risk have strong impacts on their purchasing decisions. Consumer disposition to trust, reputation, privacy concerns, security concerns, the information quality of the Website, and the company's reputation, have strong effects on Internet consumers' trust in the Website. Interestingly, the presence of a third-party seal did not strongly influence consumers' trust.

/pdf/a-trust-based-consumer-decision-making-model-in-electronic-2tir3ct0zz.pdf

A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents

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

Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research/management science (OR/MS) research.

/pdf/the-digitization-of-word-of-mouth-promise-and-challenges-of-1nibi8no0b.pdf

The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

The members of electronic communities are often unrelated to each other; they may have never met and have no information on each other's reputation This kind of information is vital in electronic commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy Two complementary reputation mechanisms are investigated which rely on collaborative rating and personalized evaluation of the various ratings assigned to each user While these reputation mechanisms are developed in the context of electronic commerce, it is believed that they may have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc

/pdf/trust-management-through-reputation-mechanisms-403k31wso1.pdf

Trust management through reputation mechanisms

The members of electronic communities are often unrelated to each other, they may have never met and have no information on each other's reputation. This kind of information is vital in electronic commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. This paper proposes two complementary reputation mechanisms that rely on collaborative ratings and personalized evaluation of the various ratings assigned to each user. While these reputation mechanisms are developed in the context of electronic commerce, we believe that they may have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.

/pdf/collaborative-reputation-mechanisms-in-electronic-4yrq39wso3.pdf

Collaborative reputation mechanisms in electronic marketplaces

Members of electronic communities are often unrelated to each other, they may have never met and have no information on each other's reputation. This kind of information is vital in Electronic Commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. Collaborative reputation mechanisms can provide personalized evaluations of the various ratings assigned to each user to predict their reliabilities. While these reputation mechanisms are developed in the context of electronic commerce, they are applicable in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.

Collaborative reputation mechanisms for electronic marketplaces

Software agents, semi-intelligent autonomous tools, will play an increasing role in electronic commerce applications. This paper gives an overview of the work at MIT's Media Laboratory on several types of agents for electronic commerce, ranging from consumer-to-consumer "smart" classified-ad systems to merchant agents that provide integrative negotiation capabilities, from agents that facilitate expertise brokering to distributed reputation facilities, and from point-of-sale comparison-shopping agents to mobile shopping systems.

/pdf/agent-mediated-electronic-commerce-an-mit-media-laboratory-4xto2j5ub5.pdf

Agent-mediated electronic commerce: an MIT media laboratory perspective

We introduce methods from statistical learning theory to the field of conjoint analysis for preference modeling. We present a method for estimating preference models that can be highly nonlinear and robust to noise. Like recently developed polyhedral methods for conjoint analysis, our method is based on computationally efficient optimization techniques. We compare our method with standard logistic regression, hierarchical Bayes, and the polyhedral methods using standard, widely used simulation data. The experiments show that the proposed method handles noise significantly better than both logistic regression and the recent polyhedral methods and is never worse than the best method among the three mentioned above. It can also be used for estimating nonlinearities in preference models faster and better than all other methods. Finally, a simple extension for handling heterogeneity shows promising results relative to hierarchical Bayes. The proposed method can therefore be useful, for example, for analyzing large amounts of data that are noisy or for estimating interactions among product features.

/pdf/generalized-robust-conjoint-estimation-4y7zwwsghd.pdf

Giorgos Zacharia

Papers

Trust management through reputation mechanisms

Collaborative reputation mechanisms in electronic marketplaces

Collaborative reputation mechanisms for electronic marketplaces

Agent-mediated electronic commerce: an MIT media laboratory perspective

Generalized Robust Conjoint Estimation