Blended Recommending

The concept of Blended Recommending combines advantages of conventional automated recommender systems, e.g. low user effort and high accuracy, with those of interactive information filtering, e.g. high level of control and transparency. For this, faceted filtering, an intuitive and efficient means for browsing large items spaces, is integrated with different recommender techniques in a hybrid fashion.

MyMovieMixer and HotelGuide are two examples for interactive recommender systems that demonstrate the approach: Users can select criteria from a set of filter facets which then serve as input for different recommender methods, both collaborative and content-based filtering. In addition, users can weight the criteria to express their preferences and to exert control over the hybrid recommendation process.

Related research topic


Benedikt Loepp


Further contributors

Katja Herrmanny

Former team member

Catalin-Mihai Barbu

External PhD Student

Jan Feuerbach



Enhancing an Interactive Recommendation System with Review-based Information Filtering

Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques

MyMovieMixer: Ein hybrider Recommender mit visuellem Bedienkonzept