Internet has opened a window enabling retailers to sell to anyone, anywhere and at any time. E-commerce has completely changed the way of doing business and in this context appeared the “daily deals” or “group buying” web pages: a business model which attracts millions of customers through the online sale of experiences – like a dinner or a trip – or products with a high percentage of discount, possible due to the great number of buyers. Motivated by this context of growth, in this project a benchmarking on web functionalities is done for the main group buying web pages and some general electronic marketplaces. As a result of this benchmarking a web functionality is chosen to be analyzed and developed for an e-commerce deals web: recommender systems. This leads to a second part of this project, where the main techniques to implement a recommendation system are studied and then used to generate a proof of concept for a recommender engine to work in a group buying web environment. The goal of using a recommendation system in this environment is to make the right information arrive to the right costumers. Recommender systems are valuable both for users and businesses. From a consumer perspective, they may help the users to manage the information overload of the e-commerce world. From a corporate point of view, they may contribute to the cross-sell and upsell of products. In this project, an item-based collaborative filtering approach is used to generate the recommender engine for the group buying web environment. After the model is designed, a proof of concept focused in the recommender core is implemented. At the end, some evaluation techniques for the recommender system are described.