Streaming media platforms constitute a significant source of information and entertainment for different population segments. Although major corporations have taken the lead in market share, public media companies have also started to produce and broadcast films, series, and documentaries centered on locally-created content. Moreover, beyond the purely commercial goals of major corporations, these public streaming platforms have the mission of expanding the cultural landscape of the viewers, for instance, through the exploration of content produced in other regions and other languages, especially in multicultural societies such as Switzerland. In such a context, this paper proposes a novel approach for personalized recommendations of streaming media content, focusing on serendipity and multicultural diversity, while minimizing the need for personal data sharing. The approach is based on the feature extraction from user media consumption and a combination of data-driven recommendation algorithms. The approach has been tested with real data from the public PlaySuisse streaming platform.