Merging interactive information filtering and recommender algorithms: model and concept demonstrator

Loepp, B., Herrmanny, K., and Ziegler, J. (2015). i-com, 14(1), 5–17.


To increase controllability and transparency in recommender systems, recent research has been putting more focus on integrating interactive techniques with recommender algorithms. In this paper, we propose a model of interactive recommending that structures the different interactions users can have with recommender systems. Furthermore, as a novel approach to interactive recommending, we describe a technique that combines faceted information filtering with different algorithmic recommender techniques. We refer to this approach as blended recommending. We also present an interactive movie recommender based on this approach and report on its user-centered design process, in particular an evaluation study in which we compared our system with a standard faceted filtering system. The results indicate a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.

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Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques

Loepp, B., Herrmanny, K., and Ziegler, J. (2015). Proceedings of the 33rd International Conference on Human Factors in Computing Systems (CHI ’15). New York, NY, USA: ACM.

MyMovieMixer: Ein hybrider Recommender mit visuellem Bedienkonzept

Herrmanny, K., Schering, S., Berger, R., Loepp, B., Günter, T., Hussein, T., and Ziegler, J. (2014). Mensch und Computer 2014 - Tagungsband: 14. fachübergreifende Konferenz für interaktive und kooperative Medien ; Interaktiv unterwegs, Freiräume gestalten. Berlin: De Gruyter Oldenbourg.

On User Awareness in Model-Based Collaborative Filtering Systems

Loepp, B. and Ziegler, J. (2017). Proceedings of the 1st Workshop on Awareness Interfaces and Interactions.