Recent research has unveiled the importance of online social networks for improving the quality of recommenders in several domains, what has encouraged the research community to investigate ways to better exploit the social information for recommendations. However, there is a lack of work that offers details of frameworks that allow an easy integration of social data with traditional recommendation algorithms in order to yield a straight-forward and scalable implementation of new and existing systems. With SocRecSearch we intend to bridge this gap. In particular with SocRecSearch we introduce a novel social recommender engine for online marketplaces that is built upon the well-know search engine Apache Solr. The framework offers a set of content and collaborative filtering approaches hybrid approaches to recommend items (e.g., products) to user not only in a personalized manner but also utilizing social data from the users social networks such as Facebook, Google+, or Twitter. To the best of our knowledge SocRecSearch is the first open source recommender engine for online marketplaces that relies on std. search software such as Apache Solr and is able to utilize social data from user to increase the recommender accuracy.