research-article

A Deep Bayesian Tensor-Based System for Video Recommendation

Authors:

Wei LiuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 37, Issue 1

Article No.: 7, Pages 1 - 22

https://doi.org/10.1145/3233773

Published: 13 December 2018 Publication History

Abstract

With the availability of abundant online multi-relational video information, recommender systems that can effectively exploit these sorts of data and suggest creatively interesting items will become increasingly important. Recent research illustrates that tensor models offer effective approaches for complex multi-relational data learning and missing element completion. So far, most tensor-based user clustering models have focused on the accuracy of recommendation. Given the dynamic nature of online media, recommendation in this setting is more challenging as it is difficult to capture the users’ dynamic topic distributions in sparse data settings as well as to identify unseen items as candidates of recommendation. Targeting at constructing a recommender system that can encourage more creativity, a deep Bayesian probabilistic tensor framework for tag and item recommendation is proposed. During the score ranking processes, a metric called Bayesian surprise is incorporated to increase the creativity of the recommended candidates. The new algorithm, called Deep Canonical PARAFAC Factorization (DCPF), is evaluated on both synthetic and large-scale real-world problems. An empirical study for video recommendation demonstrates the superiority of the proposed model and indicates that it can better capture the latent patterns of interactions and generates interesting recommendations based on creative tag combinations.

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Cited By

Lubos SFelfernig ATautschnig M(2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
https://doi.org/10.3389/fdata.2023.1281614
Quan YDing JGao CLi NYi LJin DLi Y(2023)Alleviating Video-length Effect for Micro-video RecommendationACM Transactions on Information Systems10.1145/361782642:2(1-24)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3617826
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
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Index Terms

A Deep Bayesian Tensor-Based System for Video Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 37, Issue 1

January 2019

435 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3289475

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 13 December 2018

Accepted: 01 June 2018

Revised: 01 June 2018

Received: 01 October 2017

Published in TOIS Volume 37, Issue 1

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Cited By

Lubos SFelfernig ATautschnig M(2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
https://doi.org/10.3389/fdata.2023.1281614
Quan YDing JGao CLi NYi LJin DLi Y(2023)Alleviating Video-length Effect for Micro-video RecommendationACM Transactions on Information Systems10.1145/361782642:2(1-24)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3617826
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Luo QYang MLi WXiao M(2023)Hyper-Laplacian Regularized Multi-View Clustering with Exclusive L21 Regularization and Tensor Log-Determinant Minimization ApproachACM Transactions on Intelligent Systems and Technology10.1145/358703414:3(1-29)Online publication date: 13-Apr-2023
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Yi JTian YZhao Y(2023)Design of Red Culture Retrieval System Based on Multimodal Data Fusion and Innovation of Communication Strategy PathIEEE Access10.1109/ACCESS.2023.333641911(134118-134125)Online publication date: 2023
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Kannan BDeepak G(2023)WCMIVR: A Web 3.0 Compliant Machine Intelligence Driven Scheme for Video RecommendationInnovations in Bio-Inspired Computing and Applications10.1007/978-3-031-27499-2_53(563-574)Online publication date: 28-Mar-2023
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Wu HLong JLi NYu DNg M(2022)Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative HypergraphsACM Transactions on Information Systems10.1145/354410541:2(1-25)Online publication date: 21-Dec-2022
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Adomavicius GBockstedt JCurley SZhang J(2021)Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference RatingsACM Transactions on Information Systems10.1145/343002839:2(1-38)Online publication date: 14-Jan-2021
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Drakopoulos GKanavos AMylonas PPintelas P(2020)Extending Fuzzy Cognitive Maps with Tensor-Based Distance MetricsMathematics10.3390/math81118988:11(1898)Online publication date: 31-Oct-2020
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