Using visual features based on MPEG-7 and deep learning for movie recommendation
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[1] Markus Schedl,et al. MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval , 2018, MMSys.
[2] Hamed Zamani,et al. Current challenges and visions in music recommender systems research , 2017, International Journal of Multimedia Information Retrieval.
[3] Paolo Cremonesi,et al. Exploring the Semantic Gap for Movie Recommendations , 2017, RecSys.
[4] Markus Schedl,et al. The effect of different video summarization models on the quality of video recommendation based on low-level visual features , 2017, CBMI.
[5] Franca Garzotto,et al. User interface patterns in recommendation-empowered content intensive multimedia applications , 2017, Multimedia Tools and Applications.
[6] Paolo Cremonesi,et al. How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? , 2016, EC-Web.
[7] Franca Garzotto,et al. Recommending Movies Based on Mise-en-Scene Design , 2016, CHI Extended Abstracts.
[8] Francesco Ricci,et al. A survey of active learning in collaborative filtering recommender systems , 2016, Comput. Sci. Rev..
[9] Mohamed Abdel-Mottaleb,et al. Fully automatic face normalization and single sample face recognition in unconstrained environments , 2016, Expert Syst. Appl..
[10] A. Cuzzocrea. Semantics Meets Big Data: Formal Models, Practical Issues, Novel Paradigms , 2016, Journal on Data Semantics.
[11] Franca Garzotto,et al. Content-Based Video Recommendation System Based on Stylistic Visual Features , 2016, Journal on Data Semantics.
[12] F. Maxwell Harper,et al. The MovieLens Datasets: History and Context , 2016, TIIS.
[13] Julian J. McAuley,et al. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.
[14] Rasoul Karimi,et al. Active Learning for Recommender Systems , 2015, KI - Künstliche Intelligenz.
[15] Franca Garzotto,et al. Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles , 2015, CHItaly.
[16] Franca Garzotto,et al. Interaction Design Patterns in Recommender Systems , 2015, CHItaly.
[17] Amin Mantrach,et al. Item cold-start recommendations: learning local collective embeddings , 2014, RecSys '14.
[18] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Martha Larson,et al. Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..
[20] Mouzhi Ge,et al. How should I explain? A comparison of different explanation types for recommender systems , 2014, Int. J. Hum. Comput. Stud..
[21] Stathes Hadjiefthymiades,et al. Facing the cold start problem in recommender systems , 2014, Expert Syst. Appl..
[22] Xiang-yang Wang,et al. Content-based image retrieval by integrating color and texture features , 2014, Multimedia Tools and Applications.
[23] Francesco Ricci,et al. Personality-Based Active Learning for Collaborative Filtering Recommender Systems , 2013, AI*IA.
[24] Francesco Ricci,et al. Active learning strategies for rating elicitation in collaborative filtering , 2013, ACM Trans. Intell. Syst. Technol..
[25] Romit Roy Choudhury,et al. Your reactions suggest you liked the movie: automatic content rating via reaction sensing , 2013, UbiComp.
[26] George Karypis,et al. Sparse linear methods with side information for top-n recommendations , 2012, WWW.
[27] Pasquale Lops,et al. Enhanced semantic TV-show representation for personalized electronic program guides , 2012, UMAP.
[28] Zhoujun Li,et al. Integrating rich information for video recommendation with multi-task rank aggregation , 2011, ACM Multimedia.
[29] Li Li,et al. A Survey on Visual Content-Based Video Indexing and Retrieval , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[30] Franca Garzotto,et al. Looking for "Good" Recommendations: A Comparative Evaluation of Recommender Systems , 2011, INTERACT.
[31] Xavier Serra,et al. Unifying Low-Level and High-Level Music Similarity Measures , 2011, IEEE Transactions on Multimedia.
[32] James M. Rehg,et al. Movie genre classification via scene categorization , 2010, ACM Multimedia.
[33] Roberto Turrin,et al. Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.
[34] Özgür Ulusoy,et al. Bilvideo-7: an MPEG-7- compatible video indexing and retrieval system , 2010, IEEE MultiMedia.
[35] Rafael Valencia-García,et al. Solving the cold-start problem in recommender systems with social tags , 2010, Expert Syst. Appl..
[36] Iryna Gurevych,et al. Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations , 2009, TSA@CIKM.
[37] Lars Schmidt-Thieme,et al. BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.
[38] David A. McAllester,et al. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, UAI 2009.
[39] John Riedl,et al. Tagsplanations: explaining recommendations using tags , 2009, IUI.
[40] Hao Jiang,et al. Personalized online document, image and video recommendation via commodity eye-tracking , 2008, RecSys '08.
[41] Martin Szomszor,et al. Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations , 2008 .
[42] Diane J. Cook,et al. Automatic Video Classification: A Survey of the Literature , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[43] Warren Buckland,et al. What Does the Statistical Style Analysis of Film Involve? A Review of Moving into Pictures. More on Film History, Style, and Analysis , 2007, Lit. Linguistic Comput..
[44] Tao Mei,et al. Online video recommendation based on multimodal fusion and relevance feedback , 2007, CIVR '07.
[45] Vittorio Loreto,et al. Folksonomies, the semantic web, and movie recommendation , 2007 .
[46] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.
[47] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.
[48] Eduard H. Hovy,et al. Recommendations without user preferences: a natural language processing approach , 2003, IUI '03.
[49] David M. Pennock,et al. Categories and Subject Descriptors , 2001 .
[50] B. S. Manjunath,et al. Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .
[51] Svetha Venkatesh,et al. Computational Media Aesthetics: Finding Meaning Beautiful , 2001, IEEE Multim..
[52] B. S. Manjunath,et al. Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..
[53] Paolo Cremonesi,et al. Letting Users Assist What to Watch: An Interactive Query-by-Example Movie Recommendation System , 2017, IIR.
[54] Yashar Deldjoo,et al. A low-cost infrared-optical head tracking solution for virtual 3D audio environment using the Nintendo Wii-remote , 2016, Entertain. Comput..
[55] Paolo Cremonesi,et al. Polimovie: a feature-based dataset for recommender systems , 2015 .
[56] Francesco Ricci,et al. Techniques for cold-starting context-aware mobile recommender systems for tourism , 2014, Intelligenza Artificiale.
[57] Sophie Ahrens,et al. Recommender Systems , 2012 .
[58] F. Ricci,et al. System-Wide Effectiveness of Active Learning in Collaborative Filtering , 2011 .
[59] Chunxiao Xing,et al. Video Semantic Models : Survey and Evaluation* , 2006 .
[60] Yaser Sheikh,et al. On the use of computable features for film classification , 2005, IEEE Transactions on Circuits and Systems for Video Technology.
[61] Mubarak Shah,et al. Video categorization using semantics and semiotics , 2003 .
[62] Herbert Zettl,et al. Essentials of Applied Media Aesthetics , 2002 .
[63] H. Zettl. Sight, Sound, Motion: Applied Media Aesthetics , 1973 .