TagMF

TagMF represents an interactive recommending approach that merges user-generated tags with latent factors derived from user ratings to increase user profile transparency and interactive control in Recommender Systems. Taking advantage of the Matrix Factorization technique widely used in Collaborative Filtering, the method learns an integrated model of tags and latent factors, thus enabling users to understand and manipulate their preference profile expressed implicitly in the (intransparent) latent factor space through explicitly presented textual tags.

Related research topics

Contact

Benedikt Loepp

Researcher

Contributors

  • Tim Donkers
  • Timm Kleemann

Publications

Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)

Loepp, B., Donkers, T., Kleemann, T., & Ziegler, J. (to appear). International Journal of Human-Computer Studies.

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).

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.

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).