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<citation_list><citation key="ref0"><doi>10.1007/978-3-319-29659-3_4</doi><unstructured_citation>C.C. Aggarwal, &quot;Content-based recommender systems&quot;. In: Recommender systems, Springer, 2016, pp 139-166.</unstructured_citation></citation><citation key="ref1"><doi>10.1007/978-3-319-29659-3_6</doi><unstructured_citation>C. C. Aggarwal, &quot;Ensemble-based and hybrid recommender systems&quot;. In: Recommender systems, Springer, 2016, pp 199-224.</unstructured_citation></citation><citation key="ref2"><doi>10.1007/978-3-319-29659-3_7</doi><unstructured_citation>C. C. Aggarwal, &quot;Evaluating recommender systems&quot;. In: Recommender systems, Springer, pp 225-254</unstructured_citation></citation><citation key="ref3"><doi>10.1145/245108.245124</doi><unstructured_citation>M. Balabanović, and Y. Shoham, &quot;Fab: content-based, collaborative recommendation&quot;, Communications of the ACM, vol. 40, no. 3, 1997, pp. 66-72.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>L. Barrington, R. Oda, and G. Lanckriet, &quot;Smarter Than Genius? Human Evaluation of Music Recommender Systems&quot;. In Proceeding of 10th International Society for Music Information Retrieval Conference, number ISMIR, 2009. pp. 357-362.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>C. Basu, H. Hirsh, and W. Cohen. &quot;Recommendation as classification: Using social and content-based information in recommendation&quot;. California: American Association for Artificial Intelligence, 1998.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>J. Bennett, and S. Lanning, &quot;The Netflix Prize&quot;, ACM SIGKDD Explorations Newsletter - Special issue on visual analytics, vol. 9 Issue 2, 2007. pp. 51 - 52.</unstructured_citation></citation><citation key="ref7"><doi>10.1145/2652481</doi><unstructured_citation>G.Bonnin, D. Jannach, &quot;Automated generation of music playlists: survey and experiments&quot;. ACM Computing Survevs, vol. 47, no. 2, 2015. pp. 26</unstructured_citation></citation><citation key="ref8"><unstructured_citation>R. Burke, &quot;The Adaptive Web&quot;. Berlin, Heidelberg: Springer, 2007.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>R. D. Burke, K. J. Hammond, and B. C. Young, &quot;Knowledge-Based Navigation of Complex Information Spaces&quot;, 1996, pp. 462.</unstructured_citation></citation><citation key="ref10"><doi>10.1109/ICASI.2018.8394293</doi><unstructured_citation>S. H. Chang, A. Abdul, J. Chen, and H. Y. Liao, &quot;A personalized music recommendation system using convolutional neural networks approach&quot;. In Proceedings of 2018 IEEE International Conference on Applied System Invention (ICASI), 2018. pp. 47-49. IEEE.</unstructured_citation></citation><citation key="ref11"><doi>10.1145/1341531.1341561</doi><unstructured_citation>Ding, X., Liu, B., and Yu, P. S. 2008. &quot;A Holistic Lexicon-Based Approach to Opinion Mining&quot;, Web Search and Data Mining, pp. 231 - 239.</unstructured_citation></citation><citation key="ref12"><doi>10.5120/17557-8163</doi><unstructured_citation>A. Dureha, &quot;An Accurate Algorithm For Generating A Music Playlist Based on Facial Expressions&quot;, International Journal of Computer Applications, vol. 100, 2014.</unstructured_citation></citation><citation key="ref13"><doi>10.1109/ICOT.2017.8336094</doi><unstructured_citation>J. Fang, D. Grunberg, S. Luit, and Y. Wang, &quot;Development of A Music Recommendation System for Motivating Exercise&quot;. In Proceeding of 2017 International Conference on Orange Technologies (ICOT), IEEE, 2017. pp. 83-86.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>A. P. Fiske, S. Kitayama, R. Hazel Markus, and R. E. Nisbett, &quot;The Cultural Matrix of Social Psychology&quot;, 1998.</unstructured_citation></citation><citation key="ref15"><doi>10.1145/217279.215273</doi><unstructured_citation>A. Ghias, J. Logan, D. Chamberlin, and C. Brian Smith, &quot;Query by Humming&quot;. Proceedings of the Third ACM International Conference on Multimedia- MULTIMEDIA'95, 1995. pp. 231-236.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>A. Habibzad, M. ninavin, K. M. kamal, &quot;A New Algorithm to Classify Face Emotions Through Eye and Lip Feature by using Particle Swarm Optimization&quot; 2012 4th International Conference on Computer Modeling and Simulation (ICCMS 2012), IPCSIT vol.22, 2012 IACSIT Press, Singapore</unstructured_citation></citation><citation key="ref17"><doi>10.1007/978-3-540-75690-3_1</doi><unstructured_citation>A. Hadid, M. Pietikäinen, and S. Z. Li, &quot;Learning Personal Specific Facial Dynamics For Face Recognition From Videos&quot;, International Workshop on Analysis and Modeling of Faces and Gestures, 2007, pp1-15 Springer Berlin Heidelberg.</unstructured_citation></citation><citation key="ref18"><doi>10.1145/963770.963772</doi><unstructured_citation>J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. T. Riedl, J. T. &quot;Evaluating Collaborative Filtering Recommender Systems&quot;, ACM Trans. Inf. Syst., vol. 22, no. 1, 2004, pp. 5-53, doi:10.1145/963770.963772.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>A. Joshi, and R. Kaur, &quot;A Study of speech emotion recognition methods&quot;. Int. J. Comput. Sci. Mob. Comput., vol. 2, 2013, pp. 28-31</unstructured_citation></citation><citation key="ref20"><unstructured_citation>H. Kabani, S. Khan, O. Khan and S. Tadvi, &quot;Emotion based music player&quot;, International Journal of Engineering Research and General Science, vol. 3, 2015, pp. 750-6</unstructured_citation></citation><citation key="ref21"><unstructured_citation>I. Kamehkhosh, and G. B. Dietmar Jannach, &quot;How automated recommendations affect the playlist creation behaviour of users&quot;. In: Joint Proceedings of the 23rd ACM conference on intelligent user interfaces (ACM IUI 2018) workshops: intelligent music inter- faces for listening and creation (MILC), 2018, Tokyo, Japan</unstructured_citation></citation><citation key="ref22"><unstructured_citation>T. Kanade, J. F. Cohn, and Y. Tian, &quot;Comprehensive database for facial expression analysis&quot;, In Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 46-53.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>H. T. Kim, E. Kim, J. H. Lee, and C. W. Ahn, &quot;A recommender system based on genetic algorithm for music data&quot;. In Proceeding of 2nd International Conference on Computer Engineering and Technology, vol. 6, 2010, pp. V6-414. IEEE.</unstructured_citation></citation><citation key="ref24"><doi>10.1007/978-3-540-76772-5_4</doi><unstructured_citation>J. S. Lee, and J. C. Lee, J. C., &quot;Context Awareness by Case-Based Reasoning in a Music Recommendation System&quot;, UCS, vol. 4836, 2007, pp. 45 - 58.</unstructured_citation></citation><citation key="ref25"><doi>10.1109/ICCE.2015.7066352</doi><unstructured_citation>J. Lee, S. Shin, D. Jang, S. J. Jang, and K. Yoon, &quot;Music recommendation system based on usage history and automatic genre classification&quot;. In Proceeding of 2015 IEEE International Conference on Consumer Electronics (ICCE), 2015, pp. 134-135. IEEE.</unstructured_citation></citation><citation key="ref26"><doi>10.1145/2365952.2366025</doi><unstructured_citation>A. Levi, Osnat (Ossi) Mokryn, C. Diot, and N. Taft, N. &quot;Finding a Needle in a Haystack of Reviews: Cold Start Context-Based Hotel Recommender System&quot;, 6th ACM conference on Recommender Systems, 2012.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/IWW-BCI.2018.8311499</doi><unstructured_citation>C. Liu, S. Xie, X. Xie, X. Duan, W. Wang, and K. Obermayer. &quot;Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method&quot;. In Proceeding of 2018 6th International Conference on Brain-Computer Interface (BCI), 2018, pp. 1-4 IEEE</unstructured_citation></citation><citation key="ref28"><unstructured_citation>R. R. Londhe, and D. V. Pawar, &quot;Analysis of Facial Expression and Recognition Based on Statistical Approach&quot;, International Journal of Soft Computing and Engineering, vol. 2, 2012.</unstructured_citation></citation><citation key="ref29"><doi>10.1007/978-0-387-85820-3_3</doi><unstructured_citation>P. Lops, M. D. Gemmis, and G. Semeraro, &quot;Content-based recommender systems: State of the art and trends&quot;, Recommender Systems Handbook, 2011, pp. 73 - 105.</unstructured_citation></citation><citation key="ref30"><doi>10.1109/CVPRW.2010.5543262</doi><unstructured_citation>P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews. &quot;The Extended Cohn-Kanade Dataset (Ck+) A Complete Dataset For Action Unit And Emotion-Specified Expression&quot;. In Proceeding of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, IEEE, 2010, pp. 94-101</unstructured_citation></citation><citation key="ref31"><doi>10.1109/ICSSE.2010.5551816</doi><unstructured_citation>L. Luoh, C. C. Huang, and H. Y. Liu, &quot;Image Processing Based Emotion Recognition&quot;, In Proceeding of 2010 International Conference on System Science and Engineering, IEEE, 2010, pp. 491-494</unstructured_citation></citation><citation key="ref32"><doi>10.1109/ICESC.2014.9</doi><unstructured_citation>V. Makarand, and H. V. Sahasrabuddhe, &quot;Novel Approach for Music Search Using Music Contents and Human Perception&quot;. In Proceeding of 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), IEEE, 2014, pp. 1-6. IEEE.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>B. McFee, and G. Lanckriet, &quot;Hypergraph models of playlist dialects&quot;. In: Proceedings of The 13th International Society For Music Information Retrieval Conference (ISMIR), 2012, Porto, Portugal.</unstructured_citation></citation><citation key="ref34"><doi>10.1109/GCCE.2017.8229316</doi><unstructured_citation>K. Nakamura, T. Fujisawa, and T. Kyoudou, T., &quot;Music Recommendation System using Lyric Network&quot;. In Proceeding of 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), IEEE, 2017, pp. 1-2.</unstructured_citation></citation><citation key="ref35"><doi>10.5120/ijca2016909598</doi><unstructured_citation>A. R. Patel, A. Vollal, P. B. Kadam, S. Yadav and R. M. Samant, &quot;Moody Player A Mood Based Music Player&quot; Int. J. Comput. Appl., vol. 141, 2016, pp. 0975-8887</unstructured_citation></citation><citation key="ref36"><unstructured_citation>J. Rani, and K. Garg, &quot;Emotion Detection Using Facial Expressions A Review&quot;, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, 2014.</unstructured_citation></citation><citation key="ref37"><doi>10.14569/IJARAI.2015.040204</doi><unstructured_citation>J. G. Rázuri, D. Sundgren, R. Rahmani, A. Moran, I. Bonet, and A. Larsson, &quot;Speech Emotion Recognition In Emotional Feedback For Human-Robot Interaction&quot;, International Journal of Advanced Research in Artificial Intelligence, vol. 4, 2015, pp. 20-7</unstructured_citation></citation><citation key="ref38"><doi>10.1145/245108.245121</doi><unstructured_citation>P. Resnick, and H. Varian, &quot;Recommender systems&quot;, Communications of the ACM, vol. 40, no. 3, 1997. pp. 56-58.</unstructured_citation></citation><citation key="ref39"><doi>10.1109/CultureComputing.2013.42</doi><unstructured_citation>S. Sasaki, T. Hirai, H. Ohya, and S. Morishima, &quot;Affective Music Recommendation System Reflecting the Mood of Input Image&quot;. In Proceedings of 2013 International Conference on Culture and Computing (Culture Computing), IEEE, 2013, pp. 153-154.</unstructured_citation></citation><citation key="ref40"><doi>10.1145/3109859.3109934</doi><unstructured_citation>M. Schedl, P. Knees, F. Gouyon, &quot;New paths in music recommender systems research&quot;. In: Proceedings of the 11th ACM conference on recommender systems (RecSys 2017), 2017, Como, Italy.</unstructured_citation></citation><citation key="ref41"><doi>10.1145/564376.564421</doi><unstructured_citation>A. I. Schein, A. Popescul, L. H. Ungar, D. M. Pennock, &quot;Methods and Metrics For Cold-Start Recommendations&quot;. In: SIGIR'02: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, NewYork, NY, USA, 2002, pp 253-260. https://doi.org/10.1145/ 564376.564421</unstructured_citation></citation><citation key="ref42"><doi>10.1109/ICIIECS.2017.8276172</doi><unstructured_citation>K. Shah, A. Salunke, S. Dongare, and K. Antala, &quot;Recommender systems: An overview of different approaches to recommendations&quot;. In Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 2017. pp. 1-4.</unstructured_citation></citation><citation key="ref43"><doi>10.1109/TASL.2007.913035</doi><unstructured_citation>J. Shing, R. Jang, and Hong-Ru Lee, &quot;A General Framework of Progressive Filtering and Its Application to Query by Singing/Humming&quot;. IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 2, 2008, pp. 350-358.</unstructured_citation></citation><citation key="ref44"><doi>10.1145/1178723.1178735</doi><unstructured_citation>M. Slaney, and W. White, &quot;Measuring Playlist Diversity for Recommendation Systems&quot;. In: Proceedings of the 1st ACM workshop on Audio and music computing multimedia. ACM, 2006 pp 77-82</unstructured_citation></citation><citation key="ref45"><doi>10.1155/2009/421425</doi><unstructured_citation>X. Su, and T. Khoshgoftaar, &quot;A Survey Of Collaborative Filtering Techniques,&quot; Advances in Artificial Intelligence, vol. 35, 2009, pp. 19.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>P. Tambe, Y. Bagadia, T. Khalil, and Noor UlAin Shaikh, &quot;Advanced Music Player&quot;, vol. 5, 2015.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>I. Titov and R. McDonald, &quot;A Joint Model of Text and Aspect Ratings for Sentiment Summarization&quot;, Annual Meeting of the Association for Computational Linguistics, 2008, pp. 308 - 316.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>D. Turnbull, L. Barrington, and G. Lanckriet, &quot;Five Approaches to Collecting Tags for Music&quot;. In ISMIR 2008: Proceedings of the 9th International Conference of Music Information Retrieval, 2008, pp. 225-230.</unstructured_citation></citation><citation key="ref49"><doi>10.1080/10798587.2017.1332804</doi><unstructured_citation>M. Uma, and T. Sheela, T. &quot;Analysis of Collaborative Brain Computer Interface (BCI) based Personalized GUI for Differently Abled&quot;, Intelligent Automation &amp; Soft Computing, vol. 29, 2017.</unstructured_citation></citation><citation key="ref50"><doi>10.1109/ACCT.2015.72</doi><unstructured_citation>S. Vaid, P. Singh, and C. Kaur, C., &quot;EEG signal analysis for BCI interface: A review&quot;, In Proceedings of 2015 Fifth international conference on advanced computing &amp; communication technologies, IEEE, 2015. pp. 143-147.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>A. Vall, M. Quadrana, M. Schedl, G. Widmer, P. Cremonesi, &quot;The Importance of Song Context in Music Playlists&quot;. In Proceedings of the poster track of the 11th ACM conference on recommender systems (RecSys), 2017. Como, Italy.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>F. Vignoli, 2005. &quot;A Music Retrieval System Based on User-driven Similarity and its Evaluation&quot;. In International Conference on Music Information Retrieval.</unstructured_citation></citation><citation key="ref53"><doi>10.1109/GreenCom-iThings-CPSCom.2013.341</doi><unstructured_citation>L. Xiao, Y. Zheng, W. Tang, G. Yao, L. Ruan, and X. Wang, &quot;A GPU-accelerated large-scale music similarity retrieval method&quot;. In Proceedings of IEEE International Conference on Green Computing and Communications (Green Com), 2013 IEEE and Internet of Things (iThings/CPSCom), and IEEE Cyber, Physical and Social Computing, IEEE, 2013, pp. 1839-1843.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>Y. Yi-Hsuan. Music Emotion Recognition. Tayler and Francis Group, 2011.</unstructured_citation></citation><citation key="ref55"><doi>10.1109/TPAMI.2008.52</doi><unstructured_citation>Z. Zeng, M. Pantic GI, Roisman and TS. Huang, &quot;A survey of affect recognition methods Audio, visual, and spontaneous expressions&quot;, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, 2008, pp. 39-58.</unstructured_citation></citation><citation key="ref56"><doi>10.1145/2684822.2697033</doi><unstructured_citation>Y. Zhang, M. Zhang, and Y. Liu, &quot;Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation&quot;, In Proceedings of Eighth ACM International Conference on Web Search and Data Mining, 2015, pp. 435 - 440.</unstructured_citation></citation></citation_list>
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