Merging Latent Factors and Tags to Increase Interactive Control of Recommendations

Donkers, T., Loepp, B., & Ziegler, J. (2015). In Poster Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15).

Abstract

We describe a novel approach that integrates user-generated tags with standard Matrix Factorization to allow users to interactively control recommendations. The tag information is incorporated during the learning phase and relates to the automatically derived latent factors. Thus, the system can change an item’s score whenever the user adjusts a tag’s weight. We implemented a prototype and performed a user study showing that this seems to be a promising way for users to interactively manipulate the set of items recommended based on their user profile or in cold-start situations.

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Related publications

Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control

Donkers, T., Loepp, B., & Ziegler, J. (2016). In Proceedings of the 24th Conference on User Modeling Adaptation and Personalization (UMAP ’16) (pp. 169–173). New York, NY, USA: ACM.

Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags

Donkers, T., Loepp, B., & Ziegler, J. (2016). In Poster Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16).

On User Awareness in Model-Based Collaborative Filtering Systems

Loepp, B., & Ziegler, J. (2017). In Proceedings of the 1st Workshop on Awareness Interfaces and Interactions (AWARE ’17).

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Research development

TagMF

Tag-enhanced Matrix Factorization for increasing transparency and interactive control

Focus area