research-article

MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval

Authors:

Yashar Deldjoo,

Mihai Gabriel Constantin,

Bogdan Ionescu,

Paolo CremonesiAuthors Info & Claims

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

Pages 450 - 455

https://doi.org/10.1145/3204949.3208141

Published: 12 June 2018 Publication History

Abstract

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 field.

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Cited By

Raj SAgrawal PSaha SSingh BPedanekar NHong JPark J(2024)Optimizing Movie Selections: A Multi-Task, Multi-Modal Framework with Strategies for Missing Modality ChallengesProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635947(910-918)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635947
Shaukat FEjaz NAshraf ZAlnfiai MAlnahari MAlotaibi R(2024)GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced UnderstandingIEEE Access10.1109/ACCESS.2024.343339512(103176-103191)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3433395
Sulun SViana PDavies M(2024)Movie trailer genre classification using multimodal pretrained featuresExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125209258:COnline publication date: 15-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.125209
Show More Cited By

Index Terms

MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference

June 2018

604 pages

ISBN:9781450351928

DOI:10.1145/3204949

General Chair:
Pablo Cesar
Centrum Wiskunde & Informatica, The Netherlands
,
Program Chairs:
Michael Zink
University of Massachusetts Amherst
,
Niall Murray
Athlone Institute of Technology, Ireland

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

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Publication History

Published: 12 June 2018

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MMSys '18

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SIGMM

MMSys '18: 9th ACM Multimedia Systems Conference

June 12 - 15, 2018

Amsterdam, Netherlands

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Raj SAgrawal PSaha SSingh BPedanekar NHong JPark J(2024)Optimizing Movie Selections: A Multi-Task, Multi-Modal Framework with Strategies for Missing Modality ChallengesProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635947(910-918)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635947
Shaukat FEjaz NAshraf ZAlnfiai MAlnahari MAlotaibi R(2024)GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced UnderstandingIEEE Access10.1109/ACCESS.2024.343339512(103176-103191)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3433395
Sulun SViana PDavies M(2024)Movie trailer genre classification using multimodal pretrained featuresExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125209258:COnline publication date: 15-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.125209
Raj SMondal PChakder DSaha SOnoe N(2023)A Multi-modal Multi-task based Approach for Movie Recommendation2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191882(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191882
Gan BShu XQiao RWu HChen KLi HRen B(2023)Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01812(18898-18907)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.01812
Montalvo-Lezama RMontalvo-Lezama BFuentes-Pineda G(2023)Improving Transfer Learning for Movie Trailer Genre Classification using a Dual Image and Video TransformerInformation Processing & Management10.1016/j.ipm.2023.10334360:3(103343)Online publication date: May-2023
https://doi.org/10.1016/j.ipm.2023.103343
Adesh ADeepak GSanthanavijayan A(2023)SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine IntelligenceComputational Intelligence and Network Systems10.1007/978-3-031-48984-6_1(1-13)Online publication date: 16-Dec-2023
https://doi.org/10.1007/978-3-031-48984-6_1
Maître H(2023)ReferencesAesthetics in Digital Photography10.1002/9781394225972.refs(271-294)Online publication date: 14-Jul-2023
https://doi.org/10.1002/9781394225972.refs
Kumar VTripathi VPant B(2022)Learning Unsupervised Visual Representations using 3D Convolutional Autoencoder with Temporal Contrastive Modeling for Video RetrievalInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2022.7.2.0187:2(272-287)Online publication date: 14-Mar-2022
https://doi.org/10.33889/IJMEMS.2022.7.2.018
Mervitz Jde Villiers JJacobs JKloppers M(2020)Comparison of early and late fusion techniques for movie trailer genre labelling2020 IEEE 23rd International Conference on Information Fusion (FUSION)10.23919/FUSION45008.2020.9190344(1-8)Online publication date: Jul-2020
https://doi.org/10.23919/FUSION45008.2020.9190344
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