Abstract
Analyzing users’ behavior is crucial to design recommendation systems on online service in IoT environment. It was difficult, if not impossible, to trace individual user’s specific behavior in a large scale. There are so much mobile online applications to produce some services, which are also becoming more and more popular and common methods with the advent of smart phones in IoT environment. Therefore, it is possible to collect large-scale dataset on mobile service markets. In this paper, we discuss cross-recommendation depending on the data analysis with large scale data collected from online services. We classified service popularity and users into two groups: mainstream and long tail. The described cross-recommendation between the two groups depends on commonly shared users’ attributes. The findings can use infer personalized recommendation to find niche market.
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References
Anderson, C.: The Long Tail: Why the Future of Business is Selling Less of More. Hyperion, New York (2006)
Brinegar, J., Capra, R.: Managing music across multiple devices and computers. In: Proceedings of the Conference (2011)
Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: MM 2005 (2005)
Celma, O., Ramirez, M., Herrera, P.: Foafing the music: a music recommendation system based on RSS feeds and user preferences. In: ISMIR (2005)
Celma, O., Cano, P.: From hits to niches? Or how popular artists can bias music recommendation and discovery. In: Proceedings of 2nd Netflix-KDD Workshop (2008)
Foss, S., Korshunov, D., Zachary, S.: An Introduction to Heavy-Tailed and Subexponential Distributions. Springer Series in Operations Research and Financial Engineering. Springer, New York (2011)
Kilkki, K.: A practical model for analyzing long tails. First Monday 12(5), 1–10 (2007)
Lee, J.H., Downie, J.S.: Survey of music information needs, uses, and seeking behaviours: preliminary findings. In: ISMIR (2004)
Lee, J.H., Waterman, N.M.: Understanding user requirements for music information services. In: ISMIR (2012)
Lesaffre, M., Voogdt, L.D., Leman, M., Baets, B.D., Meyer, H.D., Martens, J.P.: How potential users of music search and retrieval systems describe the semantic quality of music. J. Am. Soc. Inf. Sci. Technol. 59(5), 695–707 (2008)
Levy, M., Bosteels, K.: Music recommendation and the long tail. In: RecSys (2010)
Pachet, F.: Knowledge management and musical metadata. In: Schwartz, D. (ed.) Encyclopedia of Knowledge Management. Idea Group (2005)
Park, Y., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: RecSys (2008)
Sordo, M., Laurier, C., Celma, O.: Annotating music collections: how content-based similarity helps to propagate labels. In: ISMIR (2007)
Whitman, B., Lawrence, S.: Inferring description and similarity for music from CoMSUnity metadata. In: Proceedings of the 2002 International Computer Music Conference (2002)
Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: ISMIR (2006)
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Lee, H.J., Sohn, M. (2018). Cross-Recommendation Depending on Commonly Shared Users’ Preferences in IoT Environment. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_60
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DOI: https://doi.org/10.1007/978-3-319-61542-4_60
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