MMTF-14K: A Multifaceted Movie Trailer Dataset for Recommendation and Retrieval
Sprache des Titels:
Proceedings of the 9th ACM Multimedia Systems Conference (MMSys 2018)
In this paper we propose a new dataset, i.e., the MMTF-14K multifaceted dataset. It is primarily designed for the evaluation of videobased 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 precomputed 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 field.