Mylonas, P.[Phivos],
Hellwagner, H.[Hermann],
Castells, P.[Pablo],
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Special issue on Multimedia
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SIViP(2), No. 4, December 2008, pp. xx-yy.
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0811
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Venkatesh, S.,
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'You Tube and I Find' Personalizing Multimedia Content Access,
PIEEE(96), No. 4, April 2008, pp. 697-711.
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0804
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Affective Labeling in a Content-Based Recommender System for Images,
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1302
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Anagnostopoulos, I.,
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Online Video Recommendation through Tag-Cloud Aggregation,
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IEEE DOI
1103
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Luo, X.[Xin],
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Xiong, Z.[Zhang],
Improving neighborhood based Collaborative Filtering via integrated
folksonomy information,
PRL(33), No. 3, 1 February 2012, pp. 263-270.
Elsevier DOI
1201
Personalized recommender systems; Collaborative Filtering;
Neighborhood based model; Folksonomy
BibRef
Song, S.B.[Song-Bo],
Moustafa, H.[Hassnaa],
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A Survey on Personalized TV and NGN Services through Context-Awareness,
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The advances in IPTV (Internet Protocol Television) technology enable
a new user-centric and interactive TV model, in which
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the TV dynamic and transparent.
BibRef
Song, S.B.[Song-Bo],
Moustafa, H.[Hassnaa],
Afifi, H.[Hossam],
Advanced IPTV Services Personalization Through Context-Aware Content
Recommendation,
MultMed(14), No. 6, 2012, pp. 1528-1537.
IEEE DOI
1212
BibRef
Zhao, X.J.[Xiao-Jian],
Yuan, J.[Jin],
Hong, R.C.[Ri-Chang],
Wang, M.[Meng],
Li, Z.J.[Zhou-Jun],
Chua, T.S.[Tat-Seng],
On Video Recommendation over Social Network,
MMMod12(149-160).
Springer DOI
1201
BibRef
Dror, G.,
Koenigstein, N.,
Koren, Y.,
Web-Scale Media Recommendation Systems,
PIEEE(100), No. 9, September 2012, pp. 2722-2736.
IEEE DOI
1209
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Sanchez, F.,
Alduan, M.,
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Menendez, J.M.,
Baez, O.,
Recommender System for Sport Videos Based on User Audiovisual
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IEEE DOI
1212
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Roy, S.D.,
Mei, T.,
Zeng, W.,
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MultMed(15), No. 6, 2013, pp. 1255-1267.
IEEE DOI
1309
Cross-domain media retrieval
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Wang, Z.[Zhi],
Sun, L.F.[Li-Feng],
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Li, H.,
Wu, D.,
Joint Social and Content Recommendation for User-Generated Videos in
Online Social Network,
MultMed(15), No. 3, 2013, pp. 698-709.
IEEE DOI
1303
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Sun, L.F.[Li-Feng],
Wang, X.,
Wang, Z.[Zhi],
Zhao, H.,
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Social-Aware Video Recommendation for Online Social Groups,
MultMed(19), No. 3, March 2017, pp. 609-618.
IEEE DOI
1702
Collaboration
BibRef
Hong, Y.H.[Yan-Hui],
Chen, T.[Tiandi],
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Sun, L.F.[Li-Feng],
Personalized Annotation for Mobile Photos Based on User's Social Circle,
MMMod16(I: 76-87).
Springer DOI
1601
BibRef
Wang, Z.[Zhi],
Zhu, W.W.[Wen-Wu],
Chen, M.H.[Ming-Hua],
Sun, L.F.[Li-Feng],
Yang, S.Q.[Shi-Qiang],
CPCDN: Content Delivery Powered by Context and User Intelligence,
MultMed(17), No. 1, January 2015, pp. 92-103.
IEEE DOI
1502
content management
BibRef
Wang, P.[Peng],
Sun, L.F.[Li-Feng],
Yang, S.Q.[Shi-Qang],
Smeaton, A.F.[Alan F.],
Towards Training-Free Refinement for Semantic Indexing of Visual Media,
MMMod16(I: 251-263).
Springer DOI
1601
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Amolochitis, E.[Emmanouil],
Christou, I.T.[Ioannis T.],
Tan, Z.H.[Zheng-Hua],
Implementing a Commercial-Strength Parallel Hybrid Movie
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IEEE_Int_Sys(29), No. 2, March 2014, pp. 92-96.
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1407
Databases
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Jiang, S.H.[Shu-Hui],
Qian, X.M.[Xue-Ming],
Shen, J.L.[Jia-Lie],
Fu, Y.,
Mei, T.[Tao],
Author Topic Model-Based Collaborative Filtering for Personalized POI
Recommendations,
MultMed(17), No. 6, June 2015, pp. 907-918.
IEEE DOI
1506
BibRef
Earlier: A1, A2, A3, A5, Only:
Travel Recommendation via Author Topic Model Based Collaborative
Filtering,
MMMod15(II: 392-402).
Springer DOI
1501
Cities and towns.
travel, not videos.
BibRef
Zhao, G.,
Qian, X.,
Xie, X.,
User-Service Rating Prediction by Exploring Social Users' Rating
Behaviors,
MultMed(18), No. 3, March 2016, pp. 496-506.
IEEE DOI
1603
Collaboration
BibRef
Lei, X.J.[Xiao-Jiang],
Qian, X.M.[Xue-Ming],
Zhao, G.S.[Guo-Shuai],
Rating Prediction Based on Social Sentiment From Textual Reviews,
MultMed(18), No. 9, September 2016, pp. 1910-1921.
IEEE DOI
1609
Web sites
BibRef
Zhou, P.,
Zhou, Y.,
Wu, D.,
Jin, H.,
Differentially Private Online Learning for Cloud-Based Video
Recommendation With Multimedia Big Data in Social Networks,
MultMed(18), No. 6, June 2016, pp. 1217-1229.
IEEE DOI
1605
Big data
BibRef
Zhou, P.,
Wang, K.,
Xu, J.,
Wu, D.,
Differentially-Private and Trustworthy Online Social Multimedia Big
Data Retrieval in Edge Computing,
MultMed(21), No. 3, March 2019, pp. 539-554.
IEEE DOI
1903
Big Data, data analysis, data privacy, mobile computing,
multimedia computing, social networking (online),
social mutlimedia
BibRef
Alhamid, M.F.,
Rawashdeh, M.,
Dong, H.,
Hossain, M.A.,
El Saddik, A.[Abdulmotaleb],
Exploring Latent Preferences for Context-Aware Personalized
Recommendation Systems,
HMS(46), No. 4, August 2016, pp. 615-623.
IEEE DOI
1608
collaborative filtering
BibRef
Hong, R.,
Zhang, L.,
Zhang, C.,
Zimmermann, R.,
Flickr Circles: Aesthetic Tendency Discovery by Multi-View
Regularized Topic Modeling,
MultMed(18), No. 8, August 2016, pp. 1555-1567.
IEEE DOI
1608
data mining
BibRef
Wu, J.,
Zhou, Y.P.[Yi-Peng],
Chiu, D.M.[Dah Ming],
Zhu, Z.,
Modeling Dynamics of Online Video Popularity,
MultMed(18), No. 9, September 2016, pp. 1882-1895.
IEEE DOI
1609
BibRef
Shen, J.[Junge],
Shen, J.[Jialie],
Mei, T.[Tao],
Gao, X.B.[Xin-Bo],
Landmark Reranking for Smart Travel Guide Systems by Combining and
Analyzing Diverse Media,
SMCS(46), No. 11, November 2016, pp. 1492-1504.
IEEE DOI
1609
query processing
BibRef
Shen, J.[Junge],
Cheng, Z.Y.[Zhi-Yong],
Shen, J.L.[Jia-Lie],
Mei, T.[Tao],
Gao, X.B.[Xin-Bo],
The Evolution of Research on Multimedia Travel Guide Search and
Recommender Systems,
MMMod14(II: 227-238).
Springer DOI
1405
BibRef
Kurzhals, K.,
John, M.,
Heimerl, F.,
Kuznecov, P.,
Weiskopf, D.,
Visual Movie Analytics,
MultMed(18), No. 11, November 2016, pp. 2149-2160.
IEEE DOI
1609
entertainment
BibRef
Neate, T.,
Jones, M.,
Evans, M.,
Interdevice Media:
Choreographing Content to Maximize Viewer Engagement,
Computer(49), No. 12, December 2016, pp. 42-49.
IEEE DOI
1612
Media
BibRef
Moon, S.E.[Seong-Eun],
Lee, J.S.[Jong-Seok],
Implicit Analysis of Perceptual Multimedia Experience Based on
Physiological Response: A Review,
MultMed(19), No. 2, February 2017, pp. 340-353.
IEEE DOI
1702
Manage multimedia for the user.
BibRef
Rohrbach, A.[Anna],
Torabi, A.[Atousa],
Rohrbach, M.[Marcus],
Tandon, N.[Niket],
Pal, C.[Christopher],
Larochelle, H.[Hugo],
Courville, A.[Aaron],
Schiele, B.[Bernt],
Movie Description,
IJCV(123), No. 1, May 2017, pp. 94-120.
Springer DOI
1705
BibRef
Yang, B.[Bo],
Lei, Y.[Yu],
Liu, J.M.[Ji-Ming],
Li, W.J.[Wen-Jie],
Social Collaborative Filtering by Trust,
PAMI(39), No. 8, August 2017, pp. 1633-1647.
IEEE DOI
1707
Collaboration, Computer science, Data models,
Predictive models, Social network services, Writing,
Recommender system, collaborative filtering,
matrix factorization, trust network.
BibRef
Xu, Z.,
Chen, L.,
Dai, Y.,
Chen, G.,
A Dynamic Topic Model and Matrix Factorization-Based Travel
Recommendation Method Exploiting Ubiquitous Data,
MultMed(19), No. 8, August 2017, pp. 1933-1945.
IEEE DOI
1708
Computational modeling, Data mining, Feature extraction,
Global Positioning System, History, Metadata, Trajectory,
Dynamic topic model (DTM), geotagged photo, travel recommendation.
BibRef
Lu, Z.Y.[Zi-Yu],
Wang, H.[Hao],
Mamoulis, N.[Nikos],
Tu, W.T.[Wen-Ting],
Cheung, D.W.[David W.],
Personalized location recommendation by aggregating multiple
recommenders in diversity,
GeoInfo(21), No. 3, July 2017, pp. 459-484.
WWW Link.
1708
BibRef
Trzcinski, T.,
Rokita, P.,
Predicting Popularity of Online Videos Using Support Vector
Regression,
MultMed(19), No. 11, November 2017, pp. 2561-2570.
IEEE DOI
1710
Context, Visualization, popularity prediction,
support vector regression, video, analysis
BibRef
de Carolis, B.[Berardina],
de Gemmis, M.[Marco],
Lops, P.[Pasquale],
Palestra, G.[Giuseppe],
Recognizing users feedback from non-verbal communicative acts in
conversational recommender systems,
PRL(99), No. 1, 2017, pp. 87-95.
Elsevier DOI
1710
Behavioral, analysis
BibRef
Zhang, J.,
Yang, Y.,
Tian, Q.,
Zhuo, L.,
Liu, X.,
Personalized Social Image Recommendation Method Based on
User-Image-Tag Model,
MultMed(19), No. 11, November 2017, pp. 2439-2449.
IEEE DOI
1710
Feature extraction, Media,
Multimedia communication, Semantics, Visualization, Vocabulary,
Social image, personalized recommendation, tag ranking,
tripartite graph, user-image-tag, model
BibRef
Zhang, J.,
Yang, Y.,
Zhuo, L.,
Tian, Q.,
Liang, X.,
Personalized Recommendation of Social Images by Constructing a User
Interest Tree With Deep Features and Tag Trees,
MultMed(21), No. 11, November 2019, pp. 2762-2775.
IEEE DOI
1911
Deep learning, Semantics, Feature extraction, Predictive models,
Cultural differences, Flickr, Training, Social image,
tag trees
BibRef
Yang, Y.,
Zhang, J.,
Liu, J.,
Li, J.,
Zhuo, L.,
Tag tree creation of social image for personalized recommendation,
ICIP17(2164-2168)
IEEE DOI
1803
Computational linguistics, Computer science, Indexes,
Knowledge management, Multimedia computing, Tools, Social image,
tag tree
BibRef
Mizutani, Y.[Yuri],
Yamamoto, K.[Kayoko],
A Sightseeing Spot Recommendation System That Takes into Account the
Change in Circumstances of Users,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link
1710
BibRef
Kato, Y.[Yudai],
Yamamoto, K.[Kayoko],
A Sightseeing Spot Recommendation System That Takes into Account the
Visiting Frequency of Users,
IJGI(9), No. 7, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Zhang, H.,
Ni, W.,
Li, X.,
Yang, Y.,
Modeling the Heterogeneous Duration of User Interest in
Time-Dependent Recommendation: A Hidden Semi-Markov Approach,
SMCS(48), No. 2, February 2018, pp. 177-194.
IEEE DOI
1801
Context, Cybernetics, Data models, Hidden Markov models,
Prediction algorithms, Predictive models, Recommender systems,
time-dependent recommendation
BibRef
Zhao, Z.,
Yang, Q.,
Lu, H.,
Weninger, T.,
Cai, D.,
He, X.,
Zhuang, Y.,
Social-Aware Movie Recommendation via Multimodal Network Learning,
MultMed(20), No. 2, February 2018, pp. 430-440.
IEEE DOI
1801
Heterogeneous networks, Measurement, Motion pictures,
Neural networks, Social network services, Visualization, Web sites,
social-aware movie recommendation (SMR)
BibRef
Yao, J.,
Wang, Y.,
Zhang, Y.,
Sun, J.,
Zhou, J.,
Joint Latent Dirichlet Allocation for Social Tags,
MultMed(20), No. 1, January 2018, pp. 224-237.
IEEE DOI
1801
information retrieval, meta data, recommender systems,
sampling methods, semantic networks, social networking (online),
topic models
BibRef
Ito, Y.[Yoshiki],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
Accurate Estimation of Personalized Video Preference Using Multiple
Users' Viewing Behavior,
IEICE(E101-D), No. 2, February 2018, pp. 481-490.
WWW Link.
1802
BibRef
Khazaei, E.[Elahe],
Alimohammadi, A.[Abbas],
An Automatic User Grouping Model for a Group Recommender System in
Location-Based Social Networks,
IJGI(7), No. 2, 2018, pp. xx-yy.
DOI Link
1802
BibRef
Khazaei, E.[Elahe],
Alimohammadi, A.[Abbas],
Context-Aware Group-Oriented Location Recommendation in
Location-Based Social Networks,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Horowitz, D.[Daniel],
Contreras, D.[David],
Salamó, M.[Maria],
EventAware: A mobile recommender system for events,
PRL(105), 2018, pp. 121-134.
Elsevier DOI
1804
Recommender systems, Natural language processing, Mobile technologies
BibRef
Zhou, Y.,
Gu, X.,
Wu, D.,
Chen, M.,
Chan, T.H.,
Ho, S.W.,
Statistical Study of View Preferences for Online Videos With
Cross-Platform Information,
MultMed(20), No. 6, June 2018, pp. 1512-1524.
IEEE DOI
1805
Correlation, Motion pictures,
Streaming media, Videos, YouTube, User view preference, comments,
view count
BibRef
Yang, Y.,
Xu, Y.,
Wang, E.,
Han, J.,
Yu, Z.,
Improving Existing Collaborative Filtering Recommendations via
Serendipity-Based Algorithm,
MultMed(20), No. 7, July 2018, pp. 1888-1900.
IEEE DOI
1806
Collaboration, Computer science, Data mining, Lifting equipment,
Multimedia communication, Recommender systems,
unrated items
BibRef
Zhou, Y.,
Wu, J.,
Chan, T.H.,
Ho, S.,
Chiu, D.,
Wu, D.,
Interpreting Video Recommendation Mechanisms by Mining View Count
Traces,
MultMed(20), No. 8, August 2018, pp. 2153-2165.
IEEE DOI
1808
data mining, social networking (online), video signal processing,
video recommendation mechanisms,
direct recommendation
BibRef
Garroppo, R.G.,
Ahmed, M.,
Niccolini, S.,
Dusi, M.,
A Vocabulary for Growth:
Topic Modeling of Content Popularity Evolution,
MultMed(20), No. 10, October 2018, pp. 2683-2692.
IEEE DOI
1810
data mining, pattern clustering, social networking (online),
historical dynamics, long-term popularity prediction, vocabulary,
popularity prediction
BibRef
Deldjoo, Y.[Yashar],
Elahi, M.[Mehdi],
Quadrana, M.[Massimo],
Cremonesi, P.[Paolo],
Using visual features based on MPEG-7 and deep learning for movie
recommendation,
MultInfoRetr(8), No. 4, November 2018, pp. 207-219.
Springer DOI
1812
BibRef
Sang, J.,
Yan, M.,
Xu, C.,
Understanding Dynamic Cross-OSN Associations for Cold-Start
Recommendation,
MultMed(20), No. 12, December 2018, pp. 3439-3451.
IEEE DOI
1812
data mining, recommender systems, social networking (online),
user modelling, dynamic cross-OSN associations,
cold-start recommendation
BibRef
Tan, Z.,
Zhang, Y.,
Predicting the Top-N Popular Videos via a Cross-Domain Hybrid Model,
MultMed(21), No. 1, January 2019, pp. 147-156.
IEEE DOI
1901
Videos, Predictive models, Data models,
Task analysis, YouTube, Popularity prediction,
cross-domain
BibRef
Bai, P.Z.[Pei-Zhen],
Ge, Y.[Yan],
Liu, F.L.[Fang-Ling],
Lu, H.P.[Hai-Ping],
Joint interaction with context operation for collaborative filtering,
PR(88), 2019, pp. 729-738.
Elsevier DOI
1901
BibRef
And:
Corrigendum:
PR(92), 2019, pp. 274.
Elsevier DOI
1905
Recommender system, Collaborative filtering,
Matrix factorization, Context aware, Joint interaction, Tensor
BibRef
Fu, M.,
Qu, H.,
Yi, Z.,
Lu, L.,
Liu, Y.,
A Novel Deep Learning-Based Collaborative Filtering Model for
Recommendation System,
Cyber(49), No. 3, March 2019, pp. 1084-1096.
IEEE DOI
1902
Neural networks, Semantics, Feature extraction, Training,
Correlation, Machine learning, Collaboration,
recommender system
BibRef
Zhao, G.,
Lei, X.,
Qian, X.,
Mei, T.,
Exploring Users' Internal Influence from Reviews for Social
Recommendation,
MultMed(21), No. 3, March 2019, pp. 771-781.
IEEE DOI
1903
Internet, recommender systems, sentiment analysis,
social networking (online), social sciences computing,
social network
BibRef
Du, Y.,
Fang, M.,
Yi, J.,
Xu, C.,
Cheng, J.,
Tao, D.,
Enhancing the Robustness of Neural Collaborative Filtering Systems
Under Malicious Attacks,
MultMed(21), No. 3, March 2019, pp. 555-565.
IEEE DOI
1903
collaborative filtering, learning (artificial intelligence),
neural nets, recommender systems, security of data,
malicious attacks
BibRef
Yang, P.,
Zhang, N.,
Zhang, S.,
Yu, L.,
Zhang, J.,
Shen, X.(.,
Content Popularity Prediction Towards Location-Aware Mobile Edge
Caching,
MultMed(21), No. 4, April 2019, pp. 915-929.
IEEE DOI
1903
Prediction algorithms, Heuristic algorithms, Training, Robustness,
Quality of experience, Predictive models,
location awareness
BibRef
Vall, A.[Andreu],
Quadrana, M.[Massimo],
Schedl, M.[Markus],
Widmer, G.[Gerhard],
Order, context and popularity bias in next-song recommendations,
MultInfoRetr(8), No. 2, June 2019, pp. 101-113.
Springer DOI
1906
BibRef
Wang, W.,
Zhang, G.,
Lu, J.,
Hierarchy Visualization for Group Recommender Systems,
SMCS(49), No. 6, June 2019, pp. 1152-1163.
IEEE DOI
1906
Layout, Data visualization, Springs, Peer-to-peer computing,
Recommender systems, Visualization, Adaptation models,
recommender systems (RSs)
BibRef
Wang, X.,
Tian, Y.,
Lan, R.,
Yang, W.,
Zhang, X.,
Beyond the Watching: Understanding Viewer Interactions in
Crowdsourced Live Video Broadcasting Services,
CirSysVideo(29), No. 11, November 2019, pp. 3454-3468.
IEEE DOI
1911
Streaming media, Broadcasting, Multimedia communication, Internet,
Games, IPTV, Business, Crowdsourced live video broadcasting,
modeling
BibRef
Jiang, S.H.[Shu-Hui],
Ding, Z.M.[Zheng-Ming],
Fu, Y.[Yun],
Heterogeneous Recommendation via Deep Low-Rank Sparse Collective
Factorization,
PAMI(42), No. 5, May 2020, pp. 1097-1111.
IEEE DOI
2004
Optimization, Numerical models, Sparse matrices, Motion pictures,
Stochastic processes, Collaboration, Sports, Recommendation,
low-rank decomposition
BibRef
Zhang, L.,
Yin, J.,
Li, P.,
Shang, Y.,
Zimmermann, R.,
Shao, L.,
Flickr Image Community Analytics by Deep Noise-Refined Matrix
Factorization,
MultMed(22), No. 5, May 2020, pp. 1273-1284.
IEEE DOI
2005
Flickr, Semantics, Visualization, Correlation, Task analysis,
Matrix decomposition, Machine learning, deep model, noise-refined,
community
BibRef
Ning, X.D.[Xiao-Dong],
Yac, L.[Lina],
Wang, X.Z.[Xian-Zhi],
Benatallah, B.[Boualem],
Dong, M.Q.[Man-Qing],
Zhang, S.[Shuai],
Rating prediction via generative convolutional neural networks based
regression,
PRL(132), 2020, pp. 12-20.
Elsevier DOI
2005
Movie ratings (good/bad/etc.).
Generative convolutional neural network, Rating Prediction
BibRef
Kristoffersen, M.S.,
Shepstone, S.E.,
Tan, Z.,
The Importance of Context When Recommending TV Content:
Dataset and Algorithms,
MultMed(22), No. 6, June 2020, pp. 1531-1541.
IEEE DOI
2005
TV, Meters, Recommender systems, Automobiles, Context modeling,
Complexity theory, Multimedia systems, Context awareness,
TV
BibRef
Rahimi, S.M.[Seyyed Mohammadreza],
Far, B.[Behrouz],
Wang, X.[Xin],
Behavior-based location recommendation on location-based social
networks,
GeoInfo(24), No. 3, July 2020, pp. 477-504.
WWW Link.
2006
BibRef
Kim, T.[Taewhan],
Jung, K.[Kangsoo],
Park, S.[Seog],
Sparsity Reduction Technique Using Grouping Method for Matrix
Factorization in Differentially Private Recommendation Systems,
IEICE(E103-D), No. 7, July 2020, pp. 1683-1692.
WWW Link.
2007
BibRef
Yang, L.,
Wu, D.,
Cai, Y.,
Shi, X.,
Wu, Y.,
Learning-Based User Clustering and Link Allocation for Content
Recommendation Based on D2D Multicast Communications,
MultMed(22), No. 8, August 2020, pp. 2111-2125.
IEEE DOI
2007
Clustering algorithms, Resource management,
Device-to-device communication, Multicast communication,
stochastic learning algorithm
BibRef
Zhang, L.M.[Lu-Ming],
Ju, X.M.[Xiao-Ming],
Yao, Y.Y.[Yi-Yang],
Liu, Z.G.[Zhen-Guang],
Massive-Scale Genre Communities Learning Using a Noise-Tolerant Deep
Architecture,
MultMed(22), No. 9, September 2020, pp. 2467-2478.
IEEE DOI
2008
Semantics, Visualization,
Clustering algorithms, Deep learning, Manifolds, Flickr,
aggregation network
BibRef
Ojagh, S.[Soroush],
Malek, M.R.[Mohammad Reza],
Saeedi, S.[Sara],
A Social-Aware Recommender System Based on User's Personal Smart
Devices,
IJGI(9), No. 9, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Zhong, M.,
Li, C.,
Wen, J.,
Liu, L.,
Ma, J.,
Zhang, G.,
Yang, Y.,
HIGnet: Hierarchical and Interactive Gate Networks for Item
Recommendation,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 50-61.
IEEE DOI
2010
Semantics, Logic gates, Intelligent systems, Predictive models,
Correlation, Feature extraction, Neural networks
BibRef
Leiva, M.,
Budán, M.C.D.,
Simari, G.I.,
Guidelines for the Analysis and Design of Argumentation-Based
Recommendation Systems,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 28-37.
IEEE DOI
2010
Intelligent systems, Guidelines, Recommender systems, Cognition,
Knowledge based systems, Software engineering,
Intelligent Systems
BibRef
Xiao, Y.,
Yao, L.,
Pei, Q.,
Wang, X.,
Yang, J.,
Sheng, Q.Z.,
MGNN: Mutualistic Graph Neural Network for Joint Friend and Item
Recommendation,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 7-17.
IEEE DOI
2010
Social networking (online), Biological system modeling,
Mathematical model, Neural networks, Aggregates,
Graph Neural Networks
BibRef
Yang, S.,
Wang, H.,
Zhang, C.,
Gao, Y.,
Contextual Bandits With Hidden Features to Online Recommendation via
Sparse Interactions,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 62-72.
IEEE DOI
2010
Recommender systems, Intelligent systems, Mathematical model,
Time series analysis, Context modeling, Estimation, Data models,
hidden features
BibRef
Zhang, Y.,
Liu, G.,
Liu, A.,
Zhang, Y.,
Li, Z.,
Zhang, X.,
Li, Q.,
Personalized Geographical Influence Modeling for POI Recommendation,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 18-27.
IEEE DOI
2010
Diversity reception, Intelligent systems, Tensile stress,
Data mining, Collaboration, Filtering, Feature extraction,
I.2.6.g Machine learning
BibRef
Zhang, Y.,
Tsang, I.W.,
Duan, L.,
Collaborative Generative Hashing for Marketing and Fast Cold-Start
Recommendation,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 84-95.
IEEE DOI
2010
Collaboration, Recommender systems, Binary codes, Training,
Intelligent systems, Quantization (signal)
BibRef
Chen, J.[Jin],
Lian, D.[Defu],
Zheng, K.[Kai],
Collaborative Filtering With Ranking-Based Priors on Unknown Ratings,
IEEE_Int_Sys(35), No. 5, September 2020, pp. 38-49.
IEEE DOI
2010
Collaboration, Prediction algorithms, Intelligent systems,
Optimization, Predictive models, Matrix decomposition, Estimation,
Collaborative Filtering
BibRef
Deldjoo, Y.[Yashar],
Schedl, M.[Markus],
Cremonesi, P.[Paolo],
Pasi, G.[Gabriella],
Recommender Systems Leveraging Multimedia Content,
Surveys(53), No. 5, September 2020, pp. xx-yy.
DOI Link
2010
ultimedia, machine learning, fashion, food, video,
signal processing, tourism, music, image, social media, audio,
deep learning
BibRef
Park, J.Y.[Ju-Youn],
Kim, J.H.[Jong-Hwan],
Online incremental hierarchical classification resonance network,
PR(111), 2021, pp. 107672.
Elsevier DOI
2012
Hierarchical classification, Incremental class learning,
Blocking method, Human-Computer/robot interaction, Multimedia recommendation
BibRef
Chen, X.,
Liu, D.,
Xiong, Z.,
Zha, Z.J.,
Learning and Fusing Multiple User Interest Representations for
Micro-Video and Movie Recommendations,
MultMed(23), 2021, pp. 484-496.
IEEE DOI
2012
Videos, Motion pictures, Collaboration, Fuses, Recommender systems,
Machine learning, Computational modeling, Attention mechanism,
user interest
BibRef
Li, G.,
Qiu, L.,
Yu, C.,
Cao, H.,
Liu, Y.,
Yang, C.,
IPTV Channel Zapping Recommendation With Attention Mechanism,
MultMed(23), 2021, pp. 538-549.
IEEE DOI
2102
IPTV, neural nets, recommender systems,
recommender system attention, IPTV systems, input user channel,
neural networks
BibRef
Yang, J.,
Klabjan, D.,
Bayesian Active Learning for Choice Models With Deep Gaussian
Processes,
ITS(22), No. 2, February 2021, pp. 1080-1092.
IEEE DOI
2102
Atmospheric modeling, Global Positioning System,
Analytical models, Computational modeling, Data models,
choice models
BibRef
Deldjoo, Y.[Yashar],
di Noia, T.[Tommaso],
Merra, F.A.[Felice Antonio],
A Survey on Adversarial Recommender Systems: From Attack/Defense
Strategies to Generative Adversarial Networks,
Surveys(54), No. 2, March 2021, pp. xx-yy.
DOI Link
2104
Survey, Recommender. privacy, Recommender systems, adversarial perturbation,
generative adversarial network, robustness, security,
min-max game
BibRef
Sang, L.[Lei],
Xu, M.[Min],
Qian, S.[Shengsheng],
Martin, M.[Matt],
Li, P.[Peter],
Wu, X.D.[Xin-Dong],
Context-Dependent Propagating-Based Video Recommendation in
Multimodal Heterogeneous Information Networks,
MultMed(23), 2021, pp. 2019-2032.
IEEE DOI
2107
Semantics, Collaboration, YouTube, Australia, Visualization,
Context modeling, Video recommendation, Network embedding
BibRef
Zhao, Z.[Zian],
Nie, J.[Jie],
Wang, C.L.[Cheng-Long],
Huang, L.[Lei],
Sliced Wasserstein based Canonical Correlation Analysis for
Cross-Domain Recommendation,
PRL(150), 2021, pp. 33-39.
Elsevier DOI
2109
Cross domain recommendation, Sliced wasserstein autoencoder,
Canonical correlation analysis
BibRef
Chen, Y.[Ya],
Mensah, S.[Samuel],
Ma, F.[Fei],
Wang, H.[Hao],
Jiang, Z.G.[Zhon-Gan],
Collaborative filtering grounded on knowledge graphs,
PRL(151), 2021, pp. 55-61.
Elsevier DOI
2110
Recommender system, Collaborative filtering, Knowledge graph
BibRef
Huang, W.[Wei],
Xu, R.Y.D.[Richard Yi Da],
Gaussian process latent variable model factorization for
context-aware recommender systems,
PRL(151), 2021, pp. 281-287.
Elsevier DOI
2110
Gaussian process latent variable model,
Collaborative factorization, Bayesian probabilistic modeling
BibRef
Ding, R.[Rui],
Chen, B.[Bowei],
Guo, G.[Guibing],
Yang, X.C.[Xiao-Chun],
Adversarial Path Sampling for Recommender Systems,
IEEE_Int_Sys(36), No. 6, November 2021, pp. 23-31.
IEEE DOI
2112
Training data, Semantics, Intelligent systems, Complexity theory,
Generative adversarial networks, Recommender systems
BibRef
Feng, L.[Lei],
Wei, H.X.[Hong-Xin],
Guo, Q.Y.[Qing-Yu],
Lin, Z.Y.[Zhuo-Yi],
An, B.[Bo],
Embedding-Augmented Generalized Matrix Factorization for
Recommendation With Implicit Feedback,
IEEE_Int_Sys(36), No. 6, November 2021, pp. 32-41.
IEEE DOI
2112
Encoding, Intelligent systems, Matrix decomposition, Convergence,
Training data, Recommender systems, Collaboration,
representation learning
BibRef
Han, T.Y.[Teng-Yue],
Niu, S.Z.[Shao-Zhang],
Wang, P.F.[Peng-Fei],
Multimodal-adaptive hierarchical network for multimedia sequential
recommendation,
PRL(152), 2021, pp. 10-17.
Elsevier DOI
2112
Multimedia, Multimodal, Sequential recommendation, Multimodal-adaptive
BibRef
Liang, D.G.[Ding-Ge],
Corneli, M.[Marco],
Bouveyron, C.[Charles],
Latouche, P.[Pierre],
DeepLTRS: A deep latent recommender system based on user ratings and
reviews,
PRL(152), 2021, pp. 267-274.
Elsevier DOI
2112
Recommender systems, Learning preferences or rankings,
Collaborative filtering, Topic modelling
BibRef
Cai, D.S.[De-Sheng],
Qian, S.S.[Sheng-Sheng],
Fang, Q.[Quan],
Xu, C.S.[Chang-Sheng],
Heterogeneous Hierarchical Feature Aggregation Network for
Personalized Micro-Video Recommendation,
MultMed(24), 2022, pp. 805-818.
IEEE DOI
2202
Graph neural networks, Task analysis, Semantics, Aggregates,
Data structures, Collaboration, Visualization, Heterogeneous graph,
multi-modal
BibRef
Cai, D.S.[De-Sheng],
Qian, S.S.[Sheng-Sheng],
Fang, Q.[Quan],
Hu, J.[Jun],
Ding, W.[Wenkui],
Xu, C.S.[Chang-Sheng],
Heterogeneous Graph Contrastive Learning Network for Personalized
Micro-Video Recommendation,
MultMed(25), 2023, pp. 2761-2773.
IEEE DOI
2307
Task analysis, Bipartite graph, Representation learning, Semantics,
Mutual information, Graph neural networks, Linear programming,
micro-video recommendation
BibRef
Yi, J.[Jing],
Chen, Z.Z.[Zhen-Zhong],
Multi-Modal Variational Graph Auto-Encoder for Recommendation Systems,
MultMed(24), 2022, pp. 1067-1079.
IEEE DOI
2203
Uncertainty, Feature extraction, Semantics, Collaboration,
Visualization, Fuses, Convolution, Multi-modal analysis,
recommendation systems
BibRef
Yi, J.[Jing],
Chen, Z.Z.[Zhen-Zhong],
Variational Mixture of Stochastic Experts Auto-Encoder for
Multi-Modal Recommendation,
MultMed(26), 2024, pp. 8941-8954.
IEEE DOI
2408
Task analysis, Noise, Uncertainty, Stochastic processes, Robustness,
Data models, Training, Multi-modal recommendation, variational auto-encoder
BibRef
Yi, J.[Jing],
Zhu, Y.C.[Yao-Chen],
Xie, J.Y.[Jia-Yi],
Chen, Z.Z.[Zhen-Zhong],
Cross-Modal Variational Auto-Encoder for Content-Based Micro-Video
Background Music Recommendation,
MultMed(25), No. , 2023, pp. 515-528.
IEEE DOI
2302
Videos, Visualization, Recommender systems, Semantics, Mood,
Task analysis, Pattern matching, Cross-modal matching,
recommendation systems
BibRef
Rodrigues, F.[Filipe],
Ortelli, N.[Nicola],
Bierlaire, M.[Michel],
Pereira, F.C.[Francisco Camara],
Bayesian Automatic Relevance Determination for Utility Function
Specification in Discrete Choice Models,
ITS(23), No. 4, April 2022, pp. 3126-3136.
IEEE DOI
2204
Bayes methods, Stochastic processes, Biological system modeling,
Approximation algorithms, Inference algorithms,
doubly stochastic variational inference
BibRef
Dai, Q.Y.[Quan-Yu],
Wu, X.M.[Xiao-Ming],
Fan, L.[Lu],
Li, Q.[Qimai],
Liu, H.[Han],
Zhang, X.T.[Xiao-Tong],
Wang, D.[Dan],
Lin, G.[Guli],
Yang, K.P.[Ke-Ping],
Personalized knowledge-aware recommendation with collaborative and
attentive graph convolutional networks,
PR(128), 2022, pp. 108628.
Elsevier DOI
2205
Recommender system, Graph convolutional network,
Attention mechanism, Knowledge graph
BibRef
Wei, Y.W.[Yin-Wei],
Wang, X.[Xiang],
He, X.N.[Xiang-Nan],
Nie, L.Q.[Li-Qiang],
Rui, Y.[Yong],
Chua, T.S.[Tat-Seng],
Hierarchical User Intent Graph Network for Multimedia Recommendation,
MultMed(24), 2022, pp. 2701-2712.
IEEE DOI
2206
Feature extraction, Visualization, Convolution, Semantics,
Collaboration, Recommender systems, Convolutional codes,
hierarchical graph structure
BibRef
Hao, J.[Junmei],
Dun, Y.J.[Yu-Jie],
Zhao, G.S.[Guo-Shuai],
Wu, Y.X.[Yu-Xia],
Qian, X.M.[Xue-Ming],
Annular-Graph Attention Model for Personalized Sequential
Recommendation,
MultMed(24), 2022, pp. 3381-3391.
IEEE DOI
2207
Recommender systems, Collaboration, Recurrent neural networks,
Deep learning, Computational modeling, Measurement, user preferences
BibRef
Zhen, Y.[Yan],
Liu, H.[Huan],
Sun, M.[Meiyu],
Yang, B.[Boran],
Zhang, P.[Puning],
Adaptive preference transfer for personalized IoT entity
recommendation,
PRL(162), 2022, pp. 40-46.
Elsevier DOI
2210
Internet of things, Recommendation systems, Adaptive transfer,
Graph convolutional network
BibRef
Shrivastava, R.[Rahul],
Sisodia, D.S.[Dilip Singh],
Nagwani, N.K.[Naresh Kumar],
BP, U.R.[Upendra Roy],
An optimized recommendation framework exploiting textual review based
opinion mining for generating pleasantly surprising, novel yet
relevant recommendations,
PRL(159), 2022, pp. 91-99.
Elsevier DOI
2206
Evolutionary optimization, Novelty, Opinion mining,
Popularity bias, Recommender system, Serendipity
BibRef
Wang, X.[Xin],
Chen, H.[Hong],
Zhou, Y.W.[Yu-Wei],
Ma, J.X.[Jian-Xin],
Zhu, W.W.[Wen-Wu],
Disentangled Representation Learning for Recommendation,
PAMI(45), No. 1, January 2023, pp. 408-424.
IEEE DOI
2212
Semantics, Representation learning, Recommender systems,
Visualization, Data models, Image color analysis, Footwear, recommendation
BibRef
Xie, J.Y.[Jia-Yi],
Zhu, Y.C.[Yao-Chen],
Chen, Z.Z.[Zhen-Zhong],
Micro-Video Popularity Prediction Via Multimodal Variational
Information Bottleneck,
MultMed(25), 2023, pp. 24-37.
IEEE DOI
2301
Social networking (online), Feature extraction, Visualization,
Hidden Markov models, Uncertainty, Fuses, Task analysis,
product-of-experts system
BibRef
Nie, J.[Jie],
Zhao, Z.[Zian],
Huang, L.[Lei],
Nie, W.Z.[Wei-Zhi],
Wei, Z.Q.[Zhi-Qiang],
Cross-Domain Recommendation Via User-Clustering and Multidimensional
Information Fusion,
MultMed(25), 2023, pp. 868-880.
IEEE DOI
2303
Collaboration, Fuses, Graph neural networks, Robustness,
Representation learning, Deep learning, Codes, Attention mechanism,
user-group modeling
BibRef
Wang, Y.Q.[Yu-Qing],
Qi, L.[Lianyong],
Dou, R.[Ruihan],
Shen, S.[Shigen],
Hou, L.L.[Lin-Lin],
Liu, Y.[Yuwen],
Yang, Y.H.[Yi-Hong],
Kong, L.Z.[Ling-Zhen],
An accuracy-enhanced group recommendation approach based on DEMATEL,
PRL(167), 2023, pp. 171-180.
Elsevier DOI
2303
Group recommender system, DEMATEL, Data mining, User influence,
Weight assignment
BibRef
Wang, Q.F.[Qi-Fan],
Wei, Y.W.[Yin-Wei],
Yin, J.H.[Jian-Hua],
Wu, J.L.[Jian-Long],
Song, X.[Xuemeng],
Nie, L.Q.[Li-Qiang],
DualGNN: Dual Graph Neural Network for Multimedia Recommendation,
MultMed(25), 2023, pp. 1074-1084.
IEEE DOI
2305
Representation learning, Visualization, Videos, Recommender systems,
Graph neural networks, Task analysis, representation learning
BibRef
Zhang, S.Y.[Sheng-Yu],
Feng, F.[Fuli],
Kuang, K.[Kun],
Zhang, W.Q.[Wen-Qiao],
Zhao, Z.[Zhou],
Yang, H.X.[Hong-Xia],
Chua, T.S.[Tat-Seng],
Wu, F.[Fei],
Personalized Latent Structure Learning for Recommendation,
PAMI(45), No. 8, August 2023, pp. 10285-10299.
IEEE DOI
2307
Behavioral sciences, Uncertainty, Decision making,
Probabilistic logic, Engineering profession, Estimation,
uncertainty estimation
BibRef
Prétet, L.[Laure],
Richard, G.[Gaël],
Souchier, C.[Clément],
Peeters, G.[Geoffroy],
Video-to-Music Recommendation Using Temporal Alignment of Segments,
MultMed(25), 2023, pp. 2898-2911.
IEEE DOI
2307
Videos, Task analysis, Semantics, Training, Music, Image segmentation,
Costs, Cross-modal recommendation, self-supervised learning, triplet loss
BibRef
Wu, C.W.[Chen-Wang],
Lian, D.[Defu],
Ge, Y.[Yong],
Zhu, Z.H.[Zhi-Hao],
Chen, E.[Enhong],
Influence-Driven Data Poisoning for Robust Recommender Systems,
PAMI(45), No. 10, October 2023, pp. 11915-11931.
IEEE DOI
2310
deal with biased recommendations.
BibRef
Li, M.[Ming],
Zhang, L.[Lin],
Cui, L.X.[Li-Xin],
Bai, L.[Lu],
Li, Z.[Zhao],
Wu, X.D.[Xin-Dong],
BLoG: Bootstrapped Graph Representation Learning with Local and
Global Regularization for Recommendation,
PR(144), 2023, pp. 109874.
Elsevier DOI
2310
Graph neural networks (GNN), Graph representation learning,
Graph contrastive learning, GNN-based recommender systems
BibRef
Tao, Z.[Zhulin],
Liu, X.H.[Xiao-Hao],
Xia, Y.W.[Ye-Wei],
Wang, X.[Xiang],
Yang, L.F.[Li-Fang],
Huang, X.L.[Xiang-Lin],
Chua, T.S.[Tat-Seng],
Self-Supervised Learning for Multimedia Recommendation,
MultMed(25), 2023, pp. 5107-5116.
IEEE DOI
2311
BibRef
Liu, F.[Fan],
Chen, H.L.[Hui-Lin],
Cheng, Z.Y.[Zhi-Yong],
Liu, A.[Anan],
Nie, L.Q.[Li-Qiang],
Kankanhalli, M.[Mohan],
Disentangled Multimodal Representation Learning for Recommendation,
MultMed(25), 2023, pp. 7149-7159.
IEEE DOI
2311
BibRef
Wang, Z.Y.[Zi-Yang],
Wei, W.[Wei],
Zou, D.[Ding],
Liu, Y.F.[Yi-Fan],
Li, X.L.[Xiao-Li],
Mao, X.L.[Xian-Ling],
Qiu, M.H.[Ming-Hui],
Exploring global information for session-based recommendation,
PR(145), 2024, pp. 109911.
Elsevier DOI
2311
Session-based recommendation, Graph neural network, Graph contrastive learning
BibRef
Gong, J.B.[Ji-Bing],
Zhao, Y.[Yi],
Zhao, J.Y.[Jin-Ye],
Zhang, J.[Jin],
Ma, G.X.[Gui-Xiang],
Zheng, S.J.[Shao-Jie],
Du, S.Y.[Shu-Ying],
Tang, J.[Jie],
Personalized recommendation via inductive spatiotemporal graph neural
network,
PR(145), 2024, pp. 109884.
Elsevier DOI
2311
Recommendation system, Inductive learning, Spatiotemporal graph neural network
BibRef
Wang, F.[Fuyun],
Gao, X.Y.[Xing-Yu],
Chen, Z.Y.[Zhen-Yu],
Lyu, L.[Lei],
Contrastive Multi-Level Graph Neural Networks for Session-Based
Recommendation,
MultMed(25), 2023, pp. 9278-9289.
IEEE DOI
2312
BibRef
Cao, T.W.[Tian-Wei],
Xu, Q.Q.[Qian-Qian],
Yang, Z.Y.[Zhi-Yong],
Huang, Q.M.[Qing-Ming],
Mitigating Confounding Bias in Practical Recommender Systems With
Partially Inaccessible Exposure Status,
PAMI(46), No. 2, February 2024, pp. 957-974.
IEEE DOI
2401
Recommender system, collaborative filtering, confounding bias,
debias, counterfactual learning
BibRef
Li, S.W.[Shi-Wei],
Guo, H.[Huifeng],
Tang, X.[Xing],
Tang, R.M.[Rui-Ming],
Hou, L.[Lu],
Li, R.X.[Rui-Xuan],
Zhang, R.[Rui],
Embedding Compression in Recommender Systems: A Survey,
Surveys(56), No. 5, January 2024, pp. xx-yy.
DOI Link
2402
recommender systems, embedding tables, model compression, survey
BibRef
Nie, W.Z.[Wei-Zhi],
Wen, X.[Xin],
Liu, J.[Jing],
Chen, J.W.[Jia-Wei],
Wu, J.[Jiancan],
Jin, G.Q.[Guo-Qing],
Lu, J.[Jing],
Liu, A.A.[An-An],
Knowledge-Enhanced Causal Reinforcement Learning Model for
Interactive Recommendation,
MultMed(26), 2024, pp. 1129-1142.
IEEE DOI
2402
Data models, Recommender systems, Training, Reinforcement learning,
Estimation, Correlation, Computational modeling, Causal inference,
offline reinforcement learning
BibRef
Tang, H.[Hao],
Zhao, G.[Guoshuai],
Gao, J.[Jing],
Qian, X.M.[Xue-Ming],
Personalized Representation With Contrastive Loss for Recommendation
Systems,
MultMed(26), 2024, pp. 2419-2429.
IEEE DOI
2402
Transformers, Recommender systems, Training, Task analysis,
Software engineering, Predictive models, Markov processes,
uniformity
BibRef
Wen, X.[Xin],
Nie, W.Z.[Wei-Zhi],
Liu, J.[Jing],
Su, Y.T.[Yu-Ting],
Zhang, Y.D.[Yong-Dong],
Liu, A.A.[An-An],
CDCM: ChatGPT-Aided Diversity-Aware Causal Model for Interactive
Recommendation,
MultMed(26), 2024, pp. 6488-6500.
IEEE DOI
2404
Videos, Chatbots, Semantics, Recommender systems, Data models,
Analytical models, Reinforcement learning, ChatGPT
BibRef
Zhao, W.H.[Wei-Hao],
Wu, H.[Han],
He, W.D.[Wei-Dong],
Bi, H.Y.[Hao-Yang],
Wang, H.[Hao],
Zhu, C.[Chen],
Xu, T.[Tong],
Chen, E.[Enhong],
Hierarchical Multi-Modal Attention Network for Time-Sync Comment
Video Recommendation,
CirSysVideo(34), No. 4, April 2024, pp. 2694-2705.
IEEE DOI
2404
Semantics, Streaming media, Visualization, Feature extraction,
Analytical models, Synchronization, Recommender systems,
video recommendation
BibRef
Wu, C.W.[Chen-Wang],
Lian, D.[Defu],
Ge, Y.[Yong],
Zhou, M.[Min],
Chen, E.[Enhong],
Tao, D.C.[Da-Cheng],
Boosting Factorization Machines via Saliency-Guided Mixup,
PAMI(46), No. 6, June 2024, pp. 4443-4459.
IEEE DOI
2405
Frequency modulation, Training, Adaptation models, Task analysis,
Recommender systems, Feature extraction, Data models, sparse data
BibRef
Jian, M.[Meng],
Lang, L.[Langchen],
Guo, J.J.[Jing-Jing],
Li, Z.[Zun],
Wang, T.[Tuo],
Wu, L.F.[Li-Fang],
Light dual hypergraph convolution for collaborative filtering,
PR(154), 2024, pp. 110596.
Elsevier DOI
2406
Collaborative filtering, Hypergraph, Graph convolution,
Personalized recommendation, User interest
BibRef
Liu, J.[Jing],
Sun, L.[Lele],
Nie, W.Z.[Wei-Zhi],
Su, Y.T.[Yu-Ting],
Zhang, Y.D.[Yong-Dong],
Liu, A.[Anan],
Inter- and Intra-Domain Potential User Preferences for Cross-Domain
Recommendation,
MultMed(26), 2024, pp. 8014-8025.
IEEE DOI
2408
Feature extraction, Motion pictures, Knowledge transfer, Compounds,
Training, Task analysis, Sun, Attention mechanism,
transfer learning
BibRef
Wu, D.P.[Da-Peng],
Fan, X.[Xiaming],
Zhang, P.[Puning],
Fu, M.[Miao],
Social domain integrated semantic self-discovery method for
recommendation,
PRL(184), 2024, pp. 21-27.
Elsevier DOI
2408
Recommender systems, Heterogeneous information network,
Graph representation learning, Reinforcement learning
BibRef
Guo, J.[Jie],
Wen, L.Y.[Long-Yu],
Zhou, Y.[Yan],
Song, B.[Bin],
Chi, Y.H.[Yu-Hao],
Yu, F.R.[Fei Richard],
SPACE: Self-Supervised Dual Preference Enhancing Network for
Multimodal Recommendation,
MultMed(26), 2024, pp. 8849-8859.
IEEE DOI
2408
Semantics, Self-supervised learning, Visualization, Convolution,
Task analysis, Recommender systems, Noise measurement, dual joint prediction
BibRef
Zhao, G.S.[Guo-Shuai],
Zhang, X.L.[Xiao-Long],
Tang, H.[Hao],
Shen, J.[Jialie],
Qian, X.M.[Xue-Ming],
Domain-Oriented Knowledge Transfer for Cross-Domain Recommendation,
MultMed(26), 2024, pp. 9539-9550.
IEEE DOI Code:
WWW Link.
2410
Knowledge transfer, Knowledge graphs, Semantics, Predictive models, Reviews,
Motion pictures, Bridges, Click-through rate, cold-start, recommendation system
BibRef
Bao, S.L.[Shi-Longs],
Xu, Q.Q.[Qian-Qian],
Yang, Z.Y.[Zhi-Yong],
He, Y.[Yuan],
Cao, X.C.[Xiao-Chun],
Huang, Q.M.[Qing-Ming],
Improved Diversity-Promoting Collaborative Metric Learning for
Recommendation,
PAMI(46), No. 12, December 2024, pp. 9004-9022.
IEEE DOI
2411
Measurement, Collaboration, Optimization, Vectors, Reviews,
Motion pictures, Collaborative filtering,
recommendation system
BibRef
Sugiyama, T.[Takuto],
Yoshida, S.[Soh],
Muneyasu, M.[Mitsuji],
DRGNN: Disentangled representation graph neural network for diverse
category-level recommendations,
PRL(186), 2024, pp. 78-84.
Elsevier DOI
2412
Diversified recommendation, Graph neural network,
Disentangled representation learning
BibRef
Li, Z.[Zihao],
Yang, C.[Chao],
Chen, Y.K.[Ya-Kun],
Wang, X.Z.[Xian-Zhi],
Chen, H.X.[Hong-Xu],
Xu, G.D.[Guan-Dong],
Yao, L.[Lina],
Sheng, M.[Michael],
Graph and Sequential Neural Networks in Session-based Recommendation:
A Survey,
Surveys(57), No. 2, November 2024, pp. xx-yy.
DOI Link
2501
Recommendation survey, session-based recommendation,
graph neural networks, sequential neural networks
BibRef
Liu, Q.D.[Qi-Dong],
Hu, J.X.[Jia-Xi],
Xiao, Y.T.[Yu-Tian],
Zhao, X.Y.[Xiang-Yu],
Gao, J.[Jingtong],
Wang, W.[Wanyu],
Li, Q.[Qing],
Tang, J.[Jiliang],
Multimodal Recommender Systems: A Survey,
Surveys(57), No. 2, October 2024, pp. xx-yy.
DOI Link
2501
Recommender systems, multi-modal, multi-media
BibRef
Karabila, I.[Ikram],
Darraz, N.[Nossayba],
El-Ansari, A.[Anas],
Alami, N.[Nabil],
Chenouni, S.[Salma],
El Mallahi, M.[Mostafa],
A novel E-commerce recommender system using deep learning approaches*,
ISCV24(1-8)
IEEE DOI
2408
Deep learning, Reviews, Collaborative filtering, Metadata,
User experience, Electronic commerce, Recommender system,
Neural Network
BibRef
Abdari, A.[Ali],
Falcon, A.[Alex],
Serra, G.[Giuseppe],
FArMARe: a Furniture-Aware Multi-task methodology for Recommending
Apartments based on the user interests,
CVMeta23(4295-4305)
IEEE DOI
2401
BibRef
Soumm, M.[Michaël],
Popescu, A.[Adrian],
Delezoide, B.[Bertrand],
Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation,
WACV23(2986-2996)
IEEE DOI
2302
Visualization, Data privacy, Image databases, Semantics,
Multimedia Web sites, Benchmark testing, Streaming media,
Applications: Arts/games/social media
BibRef
Yang, H.Y.[Huai-Yuan],
Zhou, H.[Hua],
Li, Y.C.[Yu-Cheng],
A Review of Academic Recommendation Systems Based on Intelligent
Recommendation Algorithms,
ICIVC22(958-962)
IEEE DOI
2301
Deep learning, Multimedia systems, Entertainment industry,
Streaming media, Motion pictures, Electronic commerce, deep learning
BibRef
Yuan, Y.[Yuan],
Tang, Y.[Yan],
Yan, Z.Q.[Zhi-Qiang],
Hu, M.[Min],
Du, L.[Luomin],
KSRG: Knowledge-Aware Sequential Recommendation with Graph Neural
Networks,
ICPR22(2408-2414)
IEEE DOI
2212
Recurrent neural networks, Heuristic algorithms, Semantics,
Markov processes, Graph neural networks
BibRef
Najmani, K.[Kawtar],
Ajallouda, L.[Lahbib],
Benlahmar, E.[El_Habib],
Sael, N.[Nawal],
Zellou, A.[Ahmed],
Offline and Online Evaluation for Recommender Systems,
ISCV22(1-5)
IEEE DOI
2208
Costs, Scalability, Decision making,
Stability analysis, Reproducibility of results,
recommender systems
BibRef
Hirakawa, T.[Taisei],
Maeda, K.[Keisuke],
Ogawa, T.[Takahiro],
Asamizu, S.[Satoshi],
Haseyama, M.[Miki],
Cross-Domain Recommendation Method Based on Multi-Layer Graph
Analysis With Visual Information,
ICIP21(2688-2692)
IEEE DOI
2201
Training, Visualization, Image processing, Graph neural networks,
History, Optimization, Cross-domain recommendation,
graph neural network
BibRef
Vrochidis, A.[Alexandros],
Dimitriou, N.[Nikolaos],
Krinidis, S.[Stelios],
Panagiotidis, S.[Savvas],
Parcharidis, S.[Stathis],
Tzovaras, D.[Dimitrios],
Video Popularity Prediction Through Fusing Early Viewership with Video
Content,
CVS21(159-168).
Springer DOI
2109
BibRef
Deldjoo, Y.[Yashar],
di Noia, T.[Tommaso],
Malitesta, D.[Daniele],
Merra, F.A.[Felice Antonio],
A Study on the Relative Importance of Convolutional Neural Networks
in Visually-Aware Recommender Systems,
CVFAD21(3956-3962)
IEEE DOI
2109
Visualization, Supervised learning, Semantics,
Feature extraction, Convolutional neural networks
BibRef
Bonet, E.R.[Esther Rodrigo],
Nguyen, D.M.[Duc Minh],
Deligiannis, N.[Nikos],
Temporal Collaborative Filtering with Graph Convolutional Neural
Networks,
ICPR21(4736-4742)
IEEE DOI
2105
Training, Recurrent neural networks, Collaborative filtering,
Data models, Graph neural networks, Trajectory
BibRef
Luo, H.,
Zhang, X.,
Guoy, G.,
Convolutional Attention Model For Restaurant Recommendation With
Multi-View Visual Features,
ICIP20(838-842)
IEEE DOI
2011
Visualization, Predictive models, Feature extraction,
Computational modeling, Analytical models, Data models, Attention mechanism
BibRef
Jiang, Y.,
Cui, K.,
Peng, B.,
Xu, C.,
Comprehensive Video Understanding: Video Summarization with
Content-Based Video Recommender Design,
CoView19(1562-1569)
IEEE DOI
2004
convolutional neural nets, feature extraction,
learning (artificial intelligence), recommender systems, scene classification
BibRef
Tanmay, K.,
Ayush, K.,
Augmented Reality Based Recommendations Based on Perceptual Shape
Style Compatibility with Objects in the Viewpoint and Color
Compatibility with the Background,
AIM19(3361-3367)
IEEE DOI
2004
augmented reality, data visualisation, recommender systems,
product textures, viewpoint image augmentation,
Personalization
BibRef
Jasani, B.,
Girdhar, R.,
Ramanan, D.,
Are we Asking the Right Questions in MovieQA?,
CLVL19(1879-1882)
IEEE DOI
2004
data visualisation,
learning (artificial intelligence),
joint language vision
BibRef
Kang, W.C.[Wang-Cheng],
Kim, E.[Eric],
Leskovec, J.[Jure],
Rosenberg, C.[Charles],
McAuley, J.[Julian],
Complete the Look: Scene-Based Complementary Product Recommendation,
CVPR19(10524-10533).
IEEE DOI
2002
BibRef
Wu, Z.P.[Zhi-Peng],
Tian, H.[Hui],
Zhu, X.Z.[Xu-Zhen],
Fan, S.S.[Shao-Shuai],
Wang, S.[Shuo],
Exploiting Incidence Relation Between Subgroups for Improving
Clustering-Based Recommendation Model,
MMMod19(I:543-555).
Springer DOI
1901
BibRef
Ji, Z.X.[Zhi-Xiang],
Tang, J.[Jie],
Wu, G.S.[Gang-Shan],
Personalized Recommendation of Photography Based on Deep Learning,
MMMod19(I:214-226).
Springer DOI
1901
BibRef
Li, Y.,
Wang, H.,
Liu, H.,
Chen, B.,
A study on content-based video recommendation,
ICIP17(4581-4585)
IEEE DOI
1803
Computational modeling, Feature extraction, Recommender systems,
Streaming media, Testing, Training, Training data,
Synthetic Anchor
BibRef
Bhatt, C.[Chidansh],
Cooper, M.[Matthew],
Zhao, J.[Jian],
SeqSense: Video Recommendation Using Topic Sequence Mining,
MMMod18(II:252-263).
Springer DOI
1802
BibRef
Lee, J.,
Abu-El-Haija, S.,
Large-Scale Content-Only Video Recommendation,
CEFR-LCV17(987-995)
IEEE DOI
1802
Feature extraction, History, Motion pictures, Neural networks,
Semantics, Visualization, YouTube
BibRef
Yin, X.,
Wang, X.,
Du, X.,
Chen, Q.,
Scale Recovery for Monocular Visual Odometry Using Depth Estimated
with Deep Convolutional Neural Fields,
ICCV17(5871-5879)
IEEE DOI
1802
cameras, convolution, distance measurement, estimation theory,
image reconstruction, image sequences,
Training
BibRef
Khan, M.W.[Mohammad Wahiduzzaman],
Chan, G.Y.[Gaik-Yee],
Chua, F.F.[Fang-Fang],
Haw, S.C.[Su-Cheng],
An Ontology-Based Hybrid Recommender System for Internet Protocol
Television,
IVIC17(131-142).
Springer DOI
1711
BibRef
Sharma, R.[Ritu],
Gopalani, D.[Dinesh],
Meena, Y.[Yogesh],
Concept-Based Approach for Research Paper Recommendation,
PReMI17(687-692).
Springer DOI
1711
BibRef
Janati, S.E.,
Maach, A.,
Towards a new adaptive E-learning framework for adapting content to
presentation,
ISCV17(1-7)
IEEE DOI
1710
computer aided instruction, handicapped aids, hypermedia, AELS-GP,
ALS, Adaptive E-Learning System,
BibRef
Talukder, M.,
Rahman, M.M.,
Halder, S.,
Uddin, M.J.,
Novel recommendation systems in social networks,
IVPR17(1-6)
IEEE DOI
1704
Computer science
BibRef
Ferdous, S.N.,
Ali, M.M.,
A semantic content based recommendation system for cross-lingual news,
IVPR17(1-6)
IEEE DOI
1704
Collaboration
BibRef
Song, M.[Mofei],
Sun, Z.X.[Zheng-Xing],
Li, B.[Bo],
Hu, J.G.[Jia-Gao],
Iterative Active Classification of Large Image Collection,
MMMod18(I:291-304).
Springer DOI
1802
BibRef
Hu, J.G.[Jia-Gao],
Sun, Z.X.[Zheng-Xing],
Li, B.[Bo],
Wang, S.[Shuang],
PicMarker: Data-Driven Image Categorization Based on Iterative
Clustering,
ACCV16(IV: 172-187).
Springer DOI
1704
BibRef
Yang, K.W.[Ke-Wei],
Sun, Z.X.[Zheng-Xing],
Wang, S.A.[Shu-Ang],
Li, B.[Bo],
Stitch-Based Image Stylization for Thread Art Using Sparse Modeling,
MMMod18(I:479-492).
Springer DOI
1802
BibRef
Hu, J.G.[Jia-Gao],
Sun, Z.X.[Zheng-Xing],
Li, B.[Bo],
Yang, K.W.[Ke-Wei],
Li, D.Y.[Dong-Yang],
Online User Modeling for Interactive Streaming Image Classification,
MMMod17(II: 293-305).
Springer DOI
1701
BibRef
Zheng, H.J.[Huang-Jie],
Yao, J.C.[Jiang-Chao],
Zhang, Y.[Ya],
Describing Geographical Characteristics with Social Images,
MMMod17(I: 115-126).
Springer DOI
1701
BibRef
Low, T.[Thomas],
Hentschel, C.[Christian],
Stober, S.[Sebastian],
Sack, H.[Harald],
Nürnberger, A.[Andreas],
Exploring Large Movie Collections:
Comparing Visual Berrypicking and Traditional Browsing,
MMMod17(II: 198-208).
Springer DOI
1701
BibRef
Tang, S.[Song],
Wu, Z.Y.[Zhi-Yong],
Chen, K.[Kang],
Movie Recommendation via BLSTM,
MMMod17(II: 269-279).
Springer DOI
1701
BibRef
Deng, Z.J.[Zi-Jun],
Zhang, F.[Fei],
Wang, S.P.S.[Sandra P. S.],
Shilling attack detection in collaborative filtering recommender
system by PCA detection and perturbation,
ICWAPR16(213-218)
IEEE DOI
1611
Collaboration
BibRef
Ma, C.,
Yan, Z.,
Chen, C.W.,
Forecasting initial popularity of just-uploaded user-generated videos,
ICIP16(474-478)
IEEE DOI
1610
Cats
BibRef
Geng, X.[Xue],
Zhang, H.W.[Han-Wang],
Bian, J.W.[Jing-Wen],
Chua, T.S.[Tat-Seng],
Learning Image and User Features for Recommendation in Social
Networks,
ICCV15(4274-4282)
IEEE DOI
1602
Learn both image features and user data.
Collaboration
BibRef
Basu, S.[Subhasree],
Yu, Y.[Yi],
Singh, V.K.[Vivek K.],
Zimmermann, R.[Roger],
Videopedia: Lecture Video Recommendation for Educational Blogs Using
Topic Modeling,
MMMod16(I: 238-250).
Springer DOI
1601
BibRef
Zhou, J.[Jiang],
Albatal, R.[Rami],
Gurrin, C.[Cathal],
Applying Visual User Interest Profiles for Recommendation and
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MMMod16(II: 361-366).
Springer DOI
1601
BibRef
Husain, M.M.[Mamo M.],
Jalab, H.A.[Hamid A.],
Rohani, V.A.[Vala Ali],
Optimized-Memory Map-Reduce Algorithm for Mobile Learning,
IVIC15(249-256).
Springer DOI
1511
BibRef
Ning, X.[Xia],
Karypis, G.[George],
Recent Advances in Recommender Systems and Future Directions,
PReMI15(3-9).
Springer DOI
1511
BibRef
Domingues, M.A.[Marcos Aurelio],
Manzato, M.G.[Marcelo Garcia],
Marcacini, R.M.[Ricardo Marcondes],
Sundermann, C.V.[Camila Vaccari],
Rezende, S.O.[Solange Oliveira],
Using Contextual Information from Topic Hierarchies to Improve
Context-Aware Recommender Systems,
ICPR14(3606-3611)
IEEE DOI
1412
Accuracy
BibRef
Manzato, M.G.[Marcelo Garcia],
Domingues, M.A.[Marcos Aurelio],
Marcacini, R.M.[Ricardo Marcondes],
Rezende, S.O.[Solange Oliveira],
Improving Personalized Ranking in Recommender Systems with Topic
Hierarchies and Implicit Feedback,
ICPR14(3696-3701)
IEEE DOI
1412
Business process re-engineering
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Zhao, G.S.[Guo-Shuai],
Qian, X.M.[Xue-Ming],
Feng, H.[He],
Personalized Recommendation by Exploring Social Users' Behaviors,
MMMod14(II: 181-191).
Springer DOI
1405
BibRef
Yoshida, T.[Taiga],
Irie, G.[Go],
Satou, T.[Takashi],
Kojima, A.[Akira],
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Improving Item Recommendation Based on Social Tag Ranking,
MMMod12(161-172).
Springer DOI
1201
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Recommendation based on Prominent Items,
ICIIP11(1-4).
IEEE DOI
1112
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Discriminative tag learning on YouTube videos with latent sub-tags,
CVPR11(3217-3224).
IEEE DOI
1106
BibRef
Toderici, G.[George],
Aradhye, H.[Hrishikesh],
Pasca, M.[Marius],
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Yagnik, J.[Jay],
Finding meaning on YouTube: Tag recommendation and category discovery,
CVPR10(3447-3454).
IEEE DOI
1006
BibRef
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Moshfeghi, Y.[Yashar],
Joho, H.[Hideo],
Ren, R.[Reede],
Hannah, D.[David],
Jose, J.M.[Joemon M.],
Enriching user profiling with affective features for the improvement of
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CIVR09(Article No 29).
DOI Link
0907
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Albanese, M.[Massimiliano],
d'Acierno, A.[Antonio],
Moscato, V.[Vincenzo],
Persia, F.[Fabio],
Picariello, A.[Antonio],
A ranking method for multimedia recommenders,
CIVR10(311-318).
DOI Link
1007
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Massa Cereda, P.R.,
Gotardo, R.A.,
Zorzo, S.D.,
Resource Recommendation Using Adaptive Automaton,
WSSIP09(1-4).
IEEE DOI
0906
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Zorzo, S.D.,
Personalization for Digital Television Using Recommendation System
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WSSIP09(1-4).
IEEE DOI
0906
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Liu, Z.[Zhu],
Gibbon, D.C.[David C.],
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Content personalization and adaptation for three-screen services,
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0807
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Yang, B.[Bo],
Mei, T.[Tao],
Hua, X.S.[Xian-Sheng],
Yang, L.J.[Lin-Jun],
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Online video recommendation based on multimodal fusion and relevance
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Yudhistira, A.S.[Agus Syawal],
Kim, M.C.[Mun-Churl],
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An Automatic Personal TV Scheduler Based on HMM for Intelligent
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PSIVT06(1123-1132).
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0612
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Metadata and User Profile Model for Personalized Enhanced DTV
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ICIP05(III: 1236-1239).
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0512
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Kim, Y.T.[Young-Tae],
Yang, S.J.[Seung-Jun],
Chang, H.S.[Hyun Sung],
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A Way of Multiplexing TV-Anytime Metadata and AV Contents to Provide
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VLBV03(148-155).
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0310
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Ferman, A.M.,
van Beek, P.J.L.[Peter J.L.],
Errico, J.H.,
Sezan, M.I.,
Multimedia content recommendation engine with automatic inference of
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ICIP03(III: 49-52).
IEEE DOI
0312
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Rambhia, A.[Avni],
Wen, J.T.[Jiang-Tao],
Cheng, S.[Spencer],
MPEG-4-Based Automatic Fine Granularity Personalization of Broadcast
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VLBV03(139-147).
Springer DOI
0310
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Advertising based on the User, Targeting .