Crowdsourced time-sync video recommendation via semantic-aware neural collaborative filtering

Zhanpeng Wu, Yan Zhou, Di Wu*, Yipeng Zhou, Jing Qin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

As an emerging type of video comments, time-sync comments (TSCs) enable viewers to make comments on video shots in a real-time manner. Such comments well reflect user interests in the frame level, which can be utilized to further improve the accuracy of video recommendation. In this paper, we make the first attempt in this direction and propose a new video recommendation algorithm called SACF by exploiting temporal relationship between time-sync comments and video frames. Our algorithm can extract a rich set of semantic features from crowdsourced time-sync comments, and combine latent semantic representations of users and videos by neural collaborative filtering. We conduct extensive experiments using real TSC datasets, and our results show that our proposed algorithm can improve the recommendation performance by 9.73% in HR@10 and 5.72% in NDCG@10 compared with other baseline solutions.

Original languageEnglish
Title of host publicationWeb Engineering
Subtitle of host publication19th International Conference, ICWE 2019, Proceedings
EditorsMaxim Bakaev, Flavius Frasincar, In-Young Ko
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages171-186
Number of pages16
ISBN (Electronic)9783030192747
ISBN (Print)9783030192730
DOIs
Publication statusPublished - 1 Jan 2019
Event19th International Conference on Web Engineering, ICWE 2019 - Daejeon, Korea, Republic of
Duration: 11 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11496 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Web Engineering, ICWE 2019
CountryKorea, Republic of
CityDaejeon
Period11/06/1914/06/19

Keywords

  • Collaborative filtering
  • Recommender system
  • Time-synchronized comment

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