Using visual features based on MPEG-7 and deep learning for movie recommendation

[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 .