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
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Journal ArticleDOI
Web analytics of user path tracing and a novel algorithm for generating recommendations in Open Journal Systems
TL;DR: This paper focuses on the analysis of user activity traces in journals using the open source software “Open Journal Systems” (OJS) and questions to what extent end users follow a certain link structure given within OJS or immediately select the articles according to their interests.
Posted Content
Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Milena Tsvetkova,Taha Yasseri,Eric T. Meyer,J. Brian Pickering,Vegard Engen,Paul Walland,Marika Lüders,Asbjørn Følstad,George Bravos +8 more
TL;DR: Current research of relevance to HMNs is reviewed across many disciplines and eight types of HMNs are identified: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration are identified.
Journal ArticleDOI
Long-term effects of user preference-oriented recommendation method on the evolution of online system
TL;DR: A novel personalized recommender based on user preferences is proposed, which allows multiple recommenders to exist in E-commerce system simultaneously and can improve the accuracy of recommendation significantly and get better trade-offs between short- and long-term performances of recommendation.
Journal ArticleDOI
Systems support for scalable data mining
TL;DR: This paper explores a migration path out of this bottlene k by onsidering an integrated hardware and software approa h to parallelize data mining and shows that parallel data mining solutions require the following components.
Proceedings ArticleDOI
Impact of recommender systems on unplanned purchase behaviours in e-commerce
TL;DR: In this article, a framework that employs a user-cantered approach to evaluate the impact of recommender systems on unplanned purchase behaviors of Chinese consumers in e-commerce is proposed.
References
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Zero defections: quality comes to services.
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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.