Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering

Hybrid Recommender Systems: Survey and Experiments

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.

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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.

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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.

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

COMMUNICATIONS OF THE ACM March 1999/Vol. 42, No. 3 81 However, the potential of the Internet for transforming commerce is largely unrealized. Electronic purchases are still largely nonautomated. While information about products and vendors is more easily accessible, and orders and payments are dealt with electronically, humans are still in the loop in all stages of the buying process, adding to transaction costs. A human buyer is still responsible for collecting and interpreting information on merchants and products, making decisions about merchants and products, and ultimately entering purchase and payment information. Software agent technologies can be used to automate several of the most time-consuming stages of the buying process. Unlike socalled traditional software, software agents are personalized, continuously running, and semiautonomous [1]. These qualities help optimize the whole buying experience, revolutionizing commerce as we know it [2]. For example, a company that needs to opular software agents were first used several years

Agents that buy and sell

Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. This paper surveys several of these agent-mediated electronic commerce systems by describing their roles in the context of a Consumer Buying Behavior (CBB) model. The CBB model we present augments traditional marketing models with concepts from Software Agents research to accommodate electronic markets. We then discuss the variety of Artificial Intelligence techniques that support agent mediation and conclude with future directions of agent-mediated electronic commerce research.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888998002082

Agent-mediated electronic commerce: a survey

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

Software agents have become very popular in the last six or so years. They have been used successfully to filter information, match people with similar interests and automate repetitive behavior. More recently, the capabilities of agents have been applied to electronic commerce, promising a revolution in the way we conduct transactions, whether business-to-business, business-to-consumer, or consumer-to-consumer. This article surveys the technologies involved in buying and selling agents and discusses several agent-mediated electronic commerce systems in the context of a general model of the buying process.

Agents that Buy and Sell: Transforming Commerce as we Know It

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.

Alexandros Moukas

Papers

Agents that buy and sell

Agent-mediated electronic commerce: a survey

Collaborative reputation mechanisms in electronic marketplaces

Agents that Buy and Sell: Transforming Commerce as we Know It

Collaborative reputation mechanisms for electronic marketplaces