Towards Interactive Recommending in Model-based Collaborative Filtering Systems

Loepp, B., & Ziegler, J. (to appear). RecSys ’19: Proceedings of the 13th ACM Conference on Recommender Systems. New York, NY, USA: ACM.

Abstract

Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today’s systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases how model-based collaborative filtering recommenders may be enhanced with advanced interaction and preference elicitation mechanisms in a holistic manner. Hereby, we underline that by employing methods we have proposed in the past it becomes possible to easily extend any matrix factorization recommender into a fully interactive, user-controlled system. By presenting and deploying our demonstrator, we aim at gathering further insights, both into how the different mechanisms may be intertwined even more closely, and how interaction behavior and resulting user experience are influenced when users can choose from these mechanisms at their own discretion.

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Research development

TagMF

Increasing transparency and offering richer interaction possibilities in today's recommender systems

Focus area