253 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 15 DOI: 10.4018/978-1-61350-044-6.ch015 INTRODUCTION In their attempt to purchase a new product, con- sumers often seek to obtain as many information as possible about it, that is capable of resolving all doubts and make sure they are doing a good purchase. Another justification for the acquisition of this information is using them to compare a product with similar products or the same category, choosing one that offers the best cost versus ben- efit. For instance, when a user has no knowledge or experience about a particular product, he or Flavius L. Gorgônio Federal University of Rio Grande do Norte, Brazil José P. Araújo Neto University of Brasília, Brazil Taciano M. Silva Federal University of Rio Grande do Norte, Brazil A Framework for Designing Recommender System for Consumers Using Distributed Data Clustering 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 fnd this kind of information is increasing, but not only to obtain additional information about a specifc 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 diffcult. 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.