research-article

Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube

Authors:

Changsheng XuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 11, Issue 2

Article No.: 31, Pages 1 - 23

https://doi.org/10.1145/2637285

Published: 07 January 2015 Publication History

Abstract

Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.

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Kowsik VYashwanth LHarish SKishore AS RJose AV D(2024)Sentiment analysis of twitter data to detect and predict political leniency using natural language processingJournal of Intelligent Information Systems10.1007/s10844-024-00842-362:3(765-785)Online publication date: 1-Jun-2024
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Index Terms

Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube
1. Information systems
  1. Information retrieval
  2. Information systems applications

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 11, Issue 2

December 2014

197 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2716635

Editor:
Ralf Steinmetz
Technische Universität Darmstadt, Germany

Issue’s Table of Contents

Copyright © 2015 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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 January 2015

Accepted: 01 May 2014

Revised: 01 March 2014

Received: 01 October 2013

Published in TOMM Volume 11, Issue 2

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Funding Sources

Beijing Natural Science Foundation (No. 4131004)
Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative
National Basic Research Program of China (No. 2012CB316304)
National Natural Science Foundation of China (No. 61225009, 61332016, 61303176)
IDM Programme Office

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Cited By

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https://doi.org/10.1007/s11469-024-01392-z
Kowsik VYashwanth LHarish SKishore AS RJose AV D(2024)Sentiment analysis of twitter data to detect and predict political leniency using natural language processingJournal of Intelligent Information Systems10.1007/s10844-024-00842-362:3(765-785)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-024-00842-3
Zhang YHara T(2024)Joint knowledge graph approach for event participant prediction with social media retweetingKnowledge and Information Systems10.1007/s10115-023-02015-066:3(2115-2133)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-02015-0
Zhang YYao LHara T(2024)Integrating Social Environment in Machine Learning Model for Debiased RecommendationMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63992-0_14(219-230)Online publication date: 19-Jul-2024
https://doi.org/10.1007/978-3-031-63992-0_14
Uludag K(2023)Personalised video recommendation system and its potential role as a trigger of addictionSCIENTIFIC STUDIOS ON SOCIAL AND POLITICAL PSYCHOLOGY10.61727/sssppj/2.2023.0452:55(4-6)Online publication date: 26-Dec-2023
https://doi.org/10.61727/sssppj/2.2023.04
Zhang YHara T(2023)Organized Event Participant Prediction Enhanced by Social Media Retweeting Data2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00038(243-248)Online publication date: 26-Oct-2023
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Zhang YHara TYao L(2023)Biased or Debiased: Polarization-aware Embedding Learning from Social Media Knowledge Graph2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191992(1-8)Online publication date: 18-Jun-2023
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Pati USwain SKumari RMishra VBandyopadhyay A(2023)Excavating Multimodal Correlation for Movie Recommendation System2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306669(1-7)Online publication date: 6-Jul-2023
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Joshi MBhuvana J(2023)Personalized Recommendation after Classification of Tweets to Predict Depression using Sentiment Analysis2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465832(827-832)Online publication date: 23-Nov-2023
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