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The MMTF-14K dataset provides a stable and extensive source for devising and evaluating movie recommender systems. MMTF-14K contains audio and visual descriptors in addition to ratings and metadata for 13,623 Hollywood-type movie trailers. The dataset therefore facilitates research on content-based recommender systems, where content refers not only to metadata, but specifically to visual and auditory characteristics of movies. The data comes also with several baselines benchmarking results for uni-modal and multi-modal recommendation systems. The dataset therefore facilitates research on movie recommendation. In addition, the rich data supports the exploration of other multimedia tasks such as popularity prediction, genre classification, or auto-tagging (aka tag prediction).

The MMTF-14K dataset has been created as a joint research work by Yashar Deldjoo (Politecnico di Milano, Italy), Mihai Gabriel Constantin (University Politehnica of Bucharest, Romania), Hamid Eghbal-Zadeh (Johannes Kepler University Linz, Austria), Bogdan Ionescu (University Politehnica of Bucharest, Romania), Markus Schedl (Johannes Kepler University Linz, Austria), and Paolo Cremonesi (Politecnico di Milano, Italy).

We would like to acknowledge MovieLens here for providing a stable benchmark dataset of movies containing individual user ratings and metadata which is an enabler for doing research on movie recommendation. Please consider the MovieLens-20M web page for more details on the ratings and tags datasets.

For further information, please see our paper at MMSys 2018 here. For acknowledgments please use one or both of the following works:

For further inquiries, feel free to contact Yashar Deldjoo through his email: deldjooy@acm.org .