Affivir: An affect-based Internet video recommendation system

被引:13
|
作者
Niu, Jianwei [1 ]
Zhao, Xiaoke [1 ]
Zhu, Like [1 ]
Li, Haiying [2 ]
机构
[1] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China
[2] Xingtai Univ, Dept Informat Sci & Technol, Xingtai 054001, Peoples R China
基金
中国国家自然科学基金;
关键词
Affective computing; Video feature extraction; User study; Video recommendation; Video clustering; CONTENT REPRESENTATION; RETRIEVAL;
D O I
10.1016/j.neucom.2012.07.050
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present Affivir, a video browing system that recommends Internet videos that match a user's affective preference. Affivir models a user's watching behavior as sessions, and dynamically adjusts session parameters to cater to the user's current mood. In each session, Affivir discovers a user's affective preference through user interactions, such as watching or skipping videos. Affivir uses video affective features (motion, shot change rate, sound energy, and audio pitch average) to retrieve videos that have similar affective responses. To efficiently search videos of interest from our video repository, all videos in the repository are pre-processed and clustered. Our experimental results show that Affivir has made a significant improvement in user satisfaction and enjoyment, compared with several other popular baseline approaches. (c) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:422 / 433
页数:12
相关论文
共 50 条
  • [1] An Affect-Based Multimodal Video Recommendation System
    Kaklauskas, Arturas
    Gudauskas, Renaldas
    Kozlovas, Matas
    Peciure, Lina
    Lepkova, Natalija
    Cerkauskas, Justas
    Banaitis, Audrius
    [J]. STUDIES IN INFORMATICS AND CONTROL, 2016, 25 (01): : 5 - 14
  • [2] An Affect-Based Video Retrieval System with Open Vocabulary Querying
    Chan, Ching Hau
    Jones, Gareth J. F.
    [J]. ADAPTIVE MULTIMEDIA RETRIEVAL: CONTEXT, EXPLORATION, AND FUSION, 2012, 6817 : 103 - 117
  • [3] An Affect-Based Built Environment Video Analytics
    Kaklauskas, A.
    Zavadskas, E. K.
    Bardauskiene, D.
    Cerkauskas, J.
    Ubarte, I.
    Seniut, M.
    Dzemyda, G.
    Kaklauskaite, M.
    Vinogradova, I.
    Velykorusova, A.
    [J]. AUTOMATION IN CONSTRUCTION, 2019, 106
  • [4] Effects of rational and social appeals of online recommendation agents on cognition- and affect-based trust
    Wang, Weiquan
    Qiu, Lingyun
    Kim, Dongmin
    Benbasat, Izak
    [J]. DECISION SUPPORT SYSTEMS, 2016, 86 : 48 - 60
  • [5] The video recommendation system based on DBN
    Cui Hongliang
    Qin Xiaona
    [J]. CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING, 2015, : 1017 - 1022
  • [6] Making Interpretation Visible With an Affect-Based Strategy
    Levine, Sarah
    [J]. READING RESEARCH QUARTERLY, 2014, 49 (03) : 283 - 303
  • [7] Stylistic Features for Affect-Based Movie Recommendations
    Tarvainen, Jussi
    Westman, Stina
    Oittinen, Pirkko
    [J]. HUMAN BEHAVIOR UNDERSTANDING (HBU 2013), 2013, 8212 : 52 - 63
  • [8] QUERIES AND TAGS IN AFFECT-BASED MULTIMEDIA RETRIEVAL
    Kierkels, Joep J. M.
    Soleymani, Mohammad
    Pun, Thieriy
    [J]. ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 1436 - 1439
  • [9] Video Recommendation System Based on Human Interest
    Jain, Shainee
    Pawar, Tejaswi
    Shah, Heth
    Morye, Omkar
    Patil, Bhushan
    [J]. PROCEEDINGS OF 2019 1ST INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION AND COMMUNICATION TECHNOLOGY (ICIICT 2019), 2019,
  • [10] User Based Efficient Video Recommendation System
    Mayan, J. Albert
    Canesaane, R. Aroul
    Jabez, J.
    Kamalesh, M. D.
    Reddy, G. Rama Mohan
    [J]. INTERNATIONAL CONFERENCE ON INTELLIGENT DATA COMMUNICATION TECHNOLOGIES AND INTERNET OF THINGS, ICICI 2018, 2019, 26 : 1307 - 1316