20.6.2.4 Movie, Video Recommender Systems, Personalization, Video on Demand

Chapter Contents (Back)
Video Servers. Recommender Systems. Video Indexing.

Mylonas, P.[Phivos], Hellwagner, H.[Hermann], Castells, P.[Pablo], Wallace, M.[Manolis],
Special issue on Multimedia semantics, adaptation and personalization: Editorial,
SIViP(2), No. 4, December 2008, pp. xx-yy.
Springer DOI 0811
BibRef

Venkatesh, S., Adams, B., Phung, D.Q., Dorai, C., Farrell, R.G., Agnihotri, L., Dimitrova, N.,
'You Tube and I Find' Personalizing Multimedia Content Access,
PIEEE(96), No. 4, April 2008, pp. 697-711.
IEEE DOI 0804
BibRef

Tkalcic, M., Odic, A., Kosir, A., Tasic, J.F.,
Affective Labeling in a Content-Based Recommender System for Images,
MultMed(15), No. 2, 2013, pp. 391-400.
IEEE DOI 1302
BibRef

Anagnostopoulos, I., Anagnostopoulos, C.N.E., Vlachogiannis, E., Gavalas, D., Tsekouras, G.,
Adaptive and personalized multimedia content delivery for disabled users in Internet TV,
SIViP(4), No. 3, September 2010, pp. 273-287.
WWW Link. 1011
BibRef

Park, J.[Jonghun], Lee, S.J.[Sang-Jin], Lee, S.J.[Sung-Jun], Kim, K.[Kwanho], Chung, B.S.[Beom-Suk], Lee, Y.K.[Yong-Ki],
Online Video Recommendation through Tag-Cloud Aggregation,
MultMedMag(18), No. 1, January-March 2011, pp. 78-87.
IEEE DOI 1103
BibRef

Luo, X.[Xin], Ouyang, Y.X.[Yuan-Xin], 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], Afifi, H.[Hossam],
A Survey on Personalized TV and NGN Services through Context-Awareness,
Surveys(44), No. 1, January 2012, pp. Article No 4.
DOI Link 1202
The advances in IPTV (Internet Protocol Television) technology enable a new user-centric and interactive TV model, in which context-awareness is promising in making the user's interaction with 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
BibRef

Sanchez, F., Alduan, M., Alvarez, F., Menendez, J.M., Baez, O.,
Recommender System for Sport Videos Based on User Audiovisual Consumption,
MultMed(14), No. 6, 2012, pp. 1546-1557.
IEEE DOI 1212
BibRef

Roy, S.D., Mei, T., Zeng, W., Li, S.,
Towards Cross-Domain Learning for Social Video Popularity Prediction,
MultMed(15), No. 6, 2013, pp. 1255-1267.
IEEE DOI 1309
Cross-domain media retrieval BibRef

Wang, Z.[Zhi], Sun, L.F.[Li-Feng], Zhu, W.W.[Wen-Wu], Yang, S.Q.[Shi-Qiang], 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
BibRef

Sun, L.F.[Li-Feng], Wang, X., Wang, Z.[Zhi], Zhao, H., Zhu, W.W.[Wen-Wu],
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], Zhang, K.[Kang], 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
BibRef

Amolochitis, E.[Emmanouil], Christou, I.T.[Ioannis T.], Tan, Z.H.[Zheng-Hua],
Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine,
IEEE_Int_Sys(29), No. 2, March 2014, pp. 92-96.
IEEE DOI 1407
Databases BibRef

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.[Ziyu], 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, Market research, 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), Market researach 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], 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.[Yaochen], 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.[Ruiming], 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


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, Pattern recognition, 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, Market research, 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 Personalisation,
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 BibRef

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], Higashino, S.[Suguru],
Improving Item Recommendation Based on Social Tag Ranking,
MMMod12(161-172).
Springer DOI 1201
BibRef

Sodhi, R.J.S.[Raminder Jeet Singh], Gaur, V.[Vaibhav], Panchal, V.K.,
Recommendation based on Prominent Items,
ICIIP11(1-4).
IEEE DOI 1112
BibRef

Yang, W.L.[Wei-Long], Toderici, G.[George],
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], Sbaiz, L.[Luciano], Yagnik, J.[Jay],
Finding meaning on YouTube: Tag recommendation and category discovery,
CVPR10(3447-3454).
IEEE DOI 1006
BibRef

Arapakis, I.[Ioannis], 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 a multimodal recommender system,
CIVR09(Article No 29).
DOI Link 0907
BibRef

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
BibRef

Massa Cereda, P.R., Gotardo, R.A., Zorzo, S.D.,
Resource Recommendation Using Adaptive Automaton,
WSSIP09(1-4).
IEEE DOI 0906
BibRef

dos Santos Lucas, A., Zorzo, S.D.,
Personalization for Digital Television Using Recommendation System strategy,
WSSIP09(1-4).
IEEE DOI 0906
BibRef

Liu, Z.[Zhu], Gibbon, D.C.[David C.], Drucker, H.[Harris], Basso, A.[Andrea],
Content personalization and adaptation for three-screen services,
CIVR08(635-644). 0807
BibRef

Yang, B.[Bo], Mei, T.[Tao], Hua, X.S.[Xian-Sheng], Yang, L.J.[Lin-Jun], Yang, S.Q.[Shi-Qiang], Li, M.J.[Ming-Jing],
Online video recommendation based on multimodal fusion and relevance feedback,
CIVR07(73-80).
DOI Link 0707
BibRef

Yudhistira, A.S.[Agus Syawal], Kim, M.C.[Mun-Churl], Kim, H.[Hieyong], Lee, H.K.[Han-Kyu],
An Automatic Personal TV Scheduler Based on HMM for Intelligent Broadcasting Services,
PSIVT06(1123-1132).
Springer DOI 0612
BibRef

Tsekeridou, S.,
Metadata and User Profile Model for Personalized Enhanced DTV Multi-Service Access,
ICIP05(III: 1236-1239).
IEEE DOI 0512
BibRef

Kim, Y.T.[Young-Tae], Yang, S.J.[Seung-Jun], Chang, H.S.[Hyun Sung], Kang, K.[Kyeongok],
A Way of Multiplexing TV-Anytime Metadata and AV Contents to Provide Personalized Services in Digital Broadcasting,
VLBV03(148-155).
Springer DOI 0310
BibRef

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 user preferences,
ICIP03(III: 49-52).
IEEE DOI 0312
BibRef

Rambhia, A.[Avni], Wen, J.T.[Jiang-Tao], Cheng, S.[Spencer],
MPEG-4-Based Automatic Fine Granularity Personalization of Broadcast Multimedia Content,
VLBV03(139-147).
Springer DOI 0310
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Advertising based on the User, Targeting .


Last update:Mar 16, 2024 at 20:36:19