Merging Latent Factors and Tags to Increase Interactive Control of Recommendations

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

Thesis by Tim Donkers

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.

Resources

Related publications

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

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Towards Interactive Recommending in Model-based Collaborative Filtering Systems

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Research development

TagMF

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

Focus area