Journal ArticleDOI
E-Commerce Recommendation Applications
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TLDR
An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.Abstract:
i>Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge—either hand-coded knowledge provided by experts or “mined” knowledge learned from the behavior of consumers—to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recommender systems, and examine privacy implications of recommender systems technology.read more
Citations
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Proceedings ArticleDOI
A reputation-based approach for choosing reliable resources in peer-to-peer networks
Ernesto Damiani,De Capitani di Vimercati,Stefano Paraboschi,Pierangela Samarati,Fabio Violante +4 more
TL;DR: This work proposes a self-regulating system where the P2P network is used to implement a robust reputation mechanism, and a distributed polling algorithm by which resource requestors can assess the reliability of a resource offered by a participant before initiating the download.
Journal ArticleDOI
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
TL;DR: This article proposes to deal with the sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback to solve the problem of sparse transactional data.
Proceedings ArticleDOI
Shilling recommender systems for fun and profit
Shyong K. Lam,John Riedl +1 more
TL;DR: Four open questions are explored that may affect the effectiveness of shilling attacks on recommender systems: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked.
Proceedings ArticleDOI
Getting to know you: learning new user preferences in recommender systems
Al Mamunur Rashid,Istvan Albert,Dan Cosley,Shyong K. Lam,Sean M. McNee,Joseph A. Konstan,John Riedl +6 more
TL;DR: Six techniques that collaborative filtering recommender systems can use to learn about new users are studied, showing that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.
Journal ArticleDOI
Recommender Systems
TL;DR: In this article, a review of recent developments in recommender systems and discuss the major challenges and major challenges of recommender system and their potential impacts and future directions are discussed, and they compare and evaluate available algorithms and examine their roles in the future developments.
References
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Zero defections: quality comes to services.
TL;DR: Defection rates are not just a measure of service quality; they are also a guide for achieving it; by listening to the reasons why customers defect, managers learn exactly where the company is falling short and where to direct their resources.
Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.