154 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 7 INTRODUCTION Learners are asking for intelligent services in or- der to discover and access the content they need. The mechanism for discovering Web documents are powerful search engines, with specialized discovery services, indexes, and databases. But, a simple query can produce hundreds or thousands of results, making it practically impossible for the trainee to check the relevance of each of them. .This chapter argues that grouping the results into relevant clusters might make the learner to use them more efficiently. The chapter describes Constanta-Nicoleta Bodea Academy of Economic Studies, Romania Maria-Iuliana Dascalu Academy of Economic Studies, Romania Adina Lipai Academy of Economic Studies, Romania Clustering of the Web Search Results in Educational Recommender Systems ABSTRACT This chapter presents a meta-search approach, meant to deliver bibliography from the internet, according to trainees’ results obtained at an e-assessment task. The bibliography consists of web pages related to the knowledge gaps of the trainees. The meta-search engine is part of an education recommender system, attached to an e-assessment application for project management knowledge. Meta-search means that, for a specifc query (or mistake made by the trainee), several search mechanisms for suitable bibliography (further reading) could be applied. The lists of results delivered by the standard search mechanisms are used to build thematically homogenous groups using an ontology-based clustering algorithm. The clustering process uses an educational ontology and WordNet lexical database to create its categories. The research is presented in the context of recommender systems and their various applications to the education domain. DOI: 10.4018/978-1-61350-489-5.ch007