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http://dx.doi.org/10.9723/jksiis.2019.24.6.011

Multimedia Contents Recommendation Method using Mood Vector in Social Networks  

Moon, Chang Bae (금오공과대학교 ICT융합특성화연구센터)
Lee, Jong Yeol (금오공과대학교 컴퓨터소프트웨어공학과)
Kim, Byeong Man (금오공과대학교 컴퓨터소프트웨어공학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.24, no.6, 2019 , pp. 11-24 More about this Journal
Abstract
The tendency of buyers of web information is changing from the cost-effectiveness to the cost-satisfaction. There is such tendency in the recommendation of multimedia contents, some of which are folksonomy-based recommendation services using mood. However, there is a problem that they does not consider synonyms. In order to solve this problem, some studies have solved the problem by defining 12 moods of Thayer model as AV values (Arousal and Valence), but the recommendation performance is lower than that of a keyword-based method at the recall level 0.1. In this paper, we propose a method based on using mood vector of multimedia contents. The method can solve the synonym problem while maintaining the same performance as the keyword-based method even at the recall level 0.1. Also, for performance analysis, we compare the proposed method with an existing method based on AV value and a keyword-based method. The result shows that the proposed method outperform the existing methods.
Keywords
Multimedia Contents Mood; Cost-Satisfaction; 12 Mood Vector; Music Recommendation; Mood Tag; Social Networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Laurier, C., Sordo, M., Serra, J., and Herrera, P. (2009). Music Mood Representations from Social Tags, Proceedings of the 10th International Society for Music Information Conference, Kobe, Japan, pp. 381-386.
2 Li, J., and Wang, J. Z. (2008). Real-Time Computerized Annotation of Pictures, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 985-1002.   DOI
3 Lindstaedt, S., Morzinger, R., Sorschag, R., Pammer, V., and Thallinger, G. (2009). Automatic Image Annotation using Visual Content and Folksonomies, Multimedia Tools and Applications, 42(1), 97-113.   DOI
4 Moon, C. B., Kim, H. S., and Kim, B. M. (2013). Music Retrieval Method using Mood Tag and Music AV Tag based on Folksonomy, Journal of The Korean Institute of Information Scientists and Engineers, 40(9), 526-543.
5 Moon, C. B., Kim, H.S., Lee, H. A., and Kim, B. M., (2014). Analysis of Relationships Between Mood and Color for Different Musical Preferences, Color Research & Application, 39(4), 413-423.   DOI
6 Moon, C. B., Kim, H.S., Lee, D. W., and Kim, B. M. (2015). Mood Lighting System Reflecting Music Mood, COLOR Research and Application, 40(2), 201-212.   DOI
7 Moon, C. B., Yi, J. Y., Kim, D.-S., and Kim, B. M. (2018). Analysis of Overlapping Mood Tags Based on Synonyms, Korea Computer Congress 2018 (KCC 2018), June 20-22, Jeju, Korea, pp. 667-669.
8 Moon, C. B., Lee, J. Y., Kim, D.-S., and Kim, B. M. (2019). Multimedia Content Recommendation in Social Networks using Mood Tags and Synonyms, Multimedia Systems, to be published, 2019.
9 Moon, C. B., Lee, J. Y., Kim, D.-S., and Kim, B. M. (2019). Analysis of Mood Tags For Music Recommendation, Journal of the Korea Industrial Information Systems Research, 24(1), 13-21.   DOI
10 Ness, S. R., Theocharis, A., Tzanetakis, G., and Martins, L. G. (2019). Improving Automatic Music Tag Annotation using Stacked Generalization of Probabilistic Svm Outputs, Proceedings of the 17th ACM International Conference on Multimedia, pp. 705-708.
11 Powers, D. (2011). Evaluation: From Precision Recall and f-measure to Roc Informedness Markedness and Correlation, Journal of Machine Learning Technology, 2(1), 37-63.
12 Russel, J. A. (1980). A Circumplex Model of Affect, Journal of Personality and Social Psychology, 39(6), 1161-1178.   DOI
13 Thayer, R. E. (1990). The Biopsychology of Mood and Arousal, Oxford University Press.
14 Tso-Sutter, K., Marinho, L., and Schmidt-Thieme, L. (2008). Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms, ACM Symposium on Applied Computing, pp. 1995-1999.
15 Vojnovic, M., Cruise, J., Gunawardena, D., and Marbach, P. (2009). Ranking and Suggesting Popular Items, IEEE Transactions on Knowledge and Data Engineering, 21, 1133-1146.   DOI
16 Yang, S., Kim, S., and Ro, Y. M. (2007). Semantic Home Photo Categorization, IEEE Transactions on Circuits and Systems for Video Technology, 17(3), 324-335.   DOI
17 Yang, S., Kim, S. K., Seo, K. S., Ro, Y. M., Kim, J., and Seo, Y. S. (2007). Semantic Categorization of Digital Home Photo using Photographic Region Templates, International Journal of Information Processing and Management, 43(2), 503-514.   DOI
18 Hevner, K. (1936). Experimental Studies of the Elements of Expression in Music, The American Journal of Psychology, 48(2), 246-268.   DOI
19 Yang, S., and Ro, R. M. (2007). Photo Indexing using Person-Based Multi-feature Fusion with Temporal Context, International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM'07), pp. 257-262.
20 Chang, E., Kingshy, G., Sychay, G., and Wu, G. (2003). CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval using Bayes Point Machines, IEEE Transactions on Circuits and Systems for Video Technology, 13(1), 26-38.   DOI
21 Ji, A., Yeon, C., Kim, H., and Jo, G. (2007). Collaborative Tagging in Recommender Systems, Australasian Joint Conference on Artificial Intelligence, pp. 377-386
22 Kim, J., Lee, S., Kim, S., and Yoo, W. Y. (2011). Music Mood Classification Model based on Arousal-valence Values, Proceedings of 13th International Conference on Advanced Communication Technology (ICACT), pp. 292-295.