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.
Enhancing Privacy in Distributed Data Clustering
