Recommender systems have played a prominent role in online platforms over the last decade. These systems have been incorporated into applications ranging from e-commerce to leisure, successfully enhancing user experience. Moreover, recommender systems are now being applied to a wider diversity of emerging context applications on the Internet including social media and online platforms for communities. In this study, we present a novel collaborative filtering recommender system model. This model differentiates from other recommender system models in that it utilizes two aggregation operators, namely OWA and uninorm, to compute similarity degrees between users. We demonstrate the application of the proposed model by integrating it in the HARMONISE platform for communities in the Urban Resilience domain. The application example illustrates how the proposed model of collaborative filtering recommender system can predict content of interest to users in the platform, based not only on user preferences but also on features of their user profile.