Merging Latent Factors and Tags to Increase Interactive Control of Recommendations

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

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 itemís score whenever the user adjusts a tagís 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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