A Framework for Designing Recommender System for Consumers Using Distributed Data Clustering

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Author(s): Flavius L. Gorgônio (Federal University of Rio Grande do Norte, Brazil), José P. Araújo Neto (University of Brasília, Brazil)and Taciano M. Silva (Federal University of Rio Grande do Norte, Brazil)
Copyright: 2012
Pages: 18
Source title: Customer Relationship Management and the Social and Semantic Web: Enabling Cliens Conexus
Source Author(s)/Editor(s): Ricardo Colomo-Palacios (Østfold University College, Norway), João Varajão (University of Trás-os-Montes e Alto Douro, Portugal)and Pedro Soto-Acosta (University of Murcia, Spain)
DOI: 10.4018/978-1-61350-044-6.ch015

Keywords: Business & Management / Business Science Reference / Consumer Management / E-Commerce

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Abstract

In the past, consumers looked for information about quality of products and services with family members, friends, vendors, and experts. Currently, this reality is changing, and the number of consumers using Internet to find this kind of information is increasing, but not only to obtain additional information about a specific product, but to compare its features with other similar products. However, Internet provides a considerable amount of information through high volume of commercial sites, making the search for really useful information costly and difficult. Recommender systems are a Web social based process, performed by ordinary people, where users want to describe their degree of appreciation about items (products, services or people) based on their personal experience. This chapter proposes a framework for designing Web recommender systems that combine a meta-search engine and a data clustering strategy for product evaluation, enabling consumers to decide which products should be chosen.

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A Framework for Designing Recommender System for Consumers Using Distributed Data Clustering

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