Recommender engines (REs) also known as recommender systems are software tools and techniques providing suggestions to a user. The suggestions provided are aimed at supporting their users in various decision making processes such as what items to buy, what music to listen, what profiles to browse, or what news to read. This thesis studies the feasibility of the integration of a recommender engine as a module in a Liferay portal, and shows the process of its design and implementation using the Apache Mahout library. As such our work tackles two major problems which are: (1) the implementation of the recommender engine using the Apache Mahout library, and (2) the integration of the recommender in Liferay portal. Prior to the design of the application and the decisions made at the design stage, are shown the analysis of the requirements for the recommender system in the context of the business. The decisions made at design time are explained and the risks involved are analyzed and mitigated. Finally conclusions regarding the integration of the recommender engine in Liferay portal are made. This study was performed as part of an internship at Everis (www.everis.com).