MMTF-14K: A Multifaceted Movie Trailer Feature Dataset for Recommendation and Retrieval

Published in Proceedings of the 9th ACM Multimedia Systems Conference, 2018

Recommended citation: Yashar Deldjoo, Mihai Gabriel Constantin, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi Proceedings of the 9th ACM Multimedia Systems Conference 2018 (MMSys 2018).

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In this paper we propose a new dataset, i.e., the MMTF-14K multi- faceted dataset. It is primarily designed for the evaluation of video- based recommender systems, but it also supports the exploration of other multimedia tasks such as popularity prediction, genre classification and auto-tagging (aka tag prediction). The data consists of 13,623 Hollywood-type movie trailers, ranked by 138,492 users, generating a total of almost 12.5 million ratings. To address a broader community, metadata, audio and visual descriptors are also pre- computed and provided along with several baseline benchmarking results for uni-modal and multi-modal recommendation systems. This creates a rich collection of data for benchmarking results and which supports future development of this eld.