Abstract
Recommendation systems suggest items and users of interest based on preferences of items or users and item or user attributes. In social media-based services of dynamic content (such as news, blog, video, movies, books, etc.), recommender systems face the problem of discovering new items, new users, and both, a problem known as a cold start problem, i.e., the incapability to provide recommendation for new items, new users, or both, due to few rating factors available in the rating matrices. To this end, we present a biclustering technique, a novel cold start recommendation method that solves the problem of identifying the new items and new users, to alleviate the dimensionality of the item-user rating matrix using biclustering technique. To overcome the information exiguity and rating diversity, it uses the smoothing and fusion technique. As discussed, the system presents content aware multimedia-based social recommender media substance from item and user bunches.
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References
F. Ricci, L. Rokach, B. Shapira, P. B. Kantor, “Recommender Systems Hand-book”, New York, NY, USA: Springer-Verlag, Oct. 2011.
D. Zhang, Q. Zou, and H. Xiong, “CRUC: Cold-start recommendations using collaborative filtering in internet of things”, Energy Proc., 2011.
Pankaj Chaudhary and A. A. Deshmukh, “A Survey of Content Aware Video based Social Recommendation System”, in Proc. IJSR, Volume 4 Issue 1, January 2015.
Hongya Zhao and Hong Yan et. al. “Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications”.
Xiaoyan Su and Taghi M. Khoshgoftaar,”A Survey of Collaborative Filtering Techniques”, in proc. of hindwai publication co., adv. In artifialinte. Volume 2009.
D. Zhang, J. Cao, M. Guo, J. Zhou, and R. Vaskar, “An efficient collaborative filtering approach using smoothing and fusing”, in Proc. ICPP, 2009.
K. W.-T. Leung and W.-C. Lee, “CLR: A collaborative location recommendation framework based on co-clustering”, in Proc. 34th Int. ACM SIGIR Conf., 2011.
Zhang et. al., “Cold Start Recommendation using Bi-Clustering and Fusion for large Scale Social Recommender Systems”, IEEE Transactions on Consumer Electronics, June 2014.
P. Victor, C. Cornelis, A. Teredesai, and M. De Cock, “Whom should I trust?: The impact of key figures on cold start recommendations”, in Proc. ACM Symp. Appl. Comput., 2008.
A. Schein, A. Popescul, L. Ungar, and D. Pennock, “Generative models for cold-start recommendations”, in Proc. SIGIR Workshop.
T. George and S. Merugu, “A scalable collaborative filtering framework based on co-clustering”, in Proc. 5th IEEE ICDM, Nov. 2005.
S. Lee, D. Lee, and S. Lee, “Personalized DTV program recommendation system under a cloud computing environment”, IEEE Trans. Consumer Electron., 2010.
C.-F. Lai, J.-H. Chang, C.-C. Hu, Y.-M. Huang, “CPRS: A cloud-based program recommendation system for digital TV platforms”, Future Generat. Comput. Syst., 2011.
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Chaudhary, P., Deshmukh, A.A., Mihovska, A., Prasad, R. (2016). NUNI (New User and New Item) Problem for SRSs Using Content Aware Multimedia-Based Approach. In: Saini, H., Sayal, R., Rawat, S. (eds) Innovations in Computer Science and Engineering. Advances in Intelligent Systems and Computing, vol 413. Springer, Singapore. https://doi.org/10.1007/978-981-10-0419-3_34
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DOI: https://doi.org/10.1007/978-981-10-0419-3_34
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