Yan Tang
ABSTRACT
The movie recommendation is one of the most active application domain for recommendation systems (RS). However, with the rapid growth in the number of films, users have vastly different needs for the quality of the movie. In addition, facing big data, the traditional stand-alone RS is incapable to meet the need of an accurate and prompt recommendation. Aiming at solving these challenges, in this paper, we first parallelize the collaborative filtering to improve the computational efficiency, then we propose a quality-aware big data based movie recommendation system.
- Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions. TKDE 17(6), 734--749 (2005) Google Scholar
Digital Library
- J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez Recommender systems survey 2013Google Scholar
- Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76--80 (2003) Google ScholarDigital Library
- Owen, S., Anil, R., Dunning, T., Friedman, E.: Mahout in Action. Manning Google ScholarDigital Library
- Lens G (2015) MoiveLens {Online}. Available: http://grouplens.org/datasets/movielensGoogle Scholar
- Thangavel SK, Thampi NS, Johnpaul CI (2013) Performance analysis of various recommendation algorithms using apache Hadoop and Mahout. Int J Sci Eng Res 4(12):279--287Google Scholar
- Collaborative Filtering Recommendation Algorithm Based on Hadoop. Proceedings of the 2011 IEEE World Congress on Services. IEEE Computer Society,Washington,DC, 490--497 Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53 Google ScholarDigital Library
- Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks. In: Proc. of the 2nd European Conf. on Computer Systems (EuroSys)., 2007. 59 72. Google ScholarDigital Library
- Dean J, Ghemawat S. Distributed programming with Mapreduce. In: Oram A, Wilson G, eds. Beautiful Code. Sebastopol: OReilly Media, Inc., 2007. 371 384.Google Scholar
- Meng-Yen Hsieh, Wen-Kuang Chou, Kuan-Ching Li: Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop. Multimedia Tools Appl. 76(3): 3383--3401 (2017) Google ScholarDigital Library
- S.Ghemawat, H. Gobioff, and S. T. Leung, "The Google File System," The 19th ACM Symposium on Operating Systems Principles, pp. 29--43, 2003 Google ScholarDigital Library
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