TagMF is a model-based Collaborative Filtering method that aims at increasing transparency and offering richer interaction possibilities in today’s Recommender Systems.

In TagMF, latent factors derived from user ratings are enhanced with additional content information, specifically tags users provided for the items. The integrated model allows to elucidate the hidden semantics of latent factors and to let users interactively control recommendations by changing the influence of the factors through easily comprehensible tags: Users can express their interests, interactively manipulate results, and critique recommended items—at cold-start when no historical data is yet available for a new user, as well as in case a long-term profile representing the current user’s preferences already exists.

Related research topic


Benedikt Loepp


Further contributors

Timm Kleemann


Tim Donkers



Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)

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

Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control

Merging Latent Factors and Tags to Increase Interactive Control of Recommendations