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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Journal ArticleDOI
Substituting Information for Interaction A Framework for Personalization in Service Encounters and Service Systems
TL;DR: “Substituting information for interaction” is suggested as a principle that unifies these different types of encounters whenever the information needed to create value in a service system accumulates incrementally through human or automated customer interactions.
Journal ArticleDOI
Multichannel personalization: Identifying consumer preferences for product recommendations in advertisements across different media channels
TL;DR: In this paper, the authors examined the ideal design of personalized product recommendations in advertisements from a consumer's perspective by relying on a choice-based conjoint experiment in the apparel industry and found that the advertising channel is the most important attribute for determining the participant's intentions to adopt the respective product recommendations, followed by the number of recommendations.
Journal ArticleDOI
A Comprehensive Survey on Travel Recommender Systems
Kinjal Chaudhari,Ankit Thakkar +1 more
TL;DR: This survey believes it would introduce a state-of-the-art travel recommender system (RS) and may be utilized to solve the existing limitations and extend its applicability.
Journal ArticleDOI
An empirical study of natural noise management in group recommendation systems
TL;DR: A model to diminish its negative effect in GRSs is developed and a case study will evaluate the results of different approaches, showing that managing the natural noise at different rating levels reduces prediction error.
Book ChapterDOI
Implementing privacy negotiations in e-commerce
TL;DR: This paper examines how service providers may resolve the trade-off between their personalization efforts and users’ individual privacy concerns and model the negotiation process as a Bayesian game where the service provider faces different types of users.
References
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TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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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.