Studies in logic, grammar and rhetoric 37(1):125–139 Kużelewska U (2014) Clustering algorithms in hybrid recommender system on MovieLens data. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. ACM Trans Knowl Discov Data 4(1):1:1–1:24 Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. Jiang H, Chen Y, Qiao WT-H, Li K-C (2015) Scaling up MapReduce-based big data processing on multi-GPU systems. Jiang H, Chen Y, Qiao Z, Li K-C, Ro W, Gaudiot J-L (2014) Accelerating MapReduce framework on multi-GPU systems. Hsieh MY, Huang TC, Hung JC, Li KC (2015) Analysis of gesture combos for social activity on smartphone. Hsieh M-Y, Yeh C-H, Tsai Y-T, Li K-C (2014) Toward a mobile application for social sharing context.
Hsieh M-Y, Tsai Y-T, Hsu C-H, Hung C-H, Li K-C (2013/5) Design and implementation of multimedia social services on Elgg. Kyushu Sangyo University, Fukuoka, Japan, The 9th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC’2012) Hsieh M-Y, Lin H-Y, Yeh C-H, Li K-C, Wu B-S (2012) A mobile application framework for rapid integration of ubiquitous web services.
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#Movie recommender app movie#
Hassanzadeh O, Consens M (2009) Linked movie data base. ACM, New York, pp 1–8įeng W, Zhang Z, Wang J, Han L (2015) A proxy re-encryption scheme of authorization delegation for multimedia social networks. In: Proceedings of the 8th International Conference on Semantic System, I-SEMANTICS’12. J Web Semant 7:154–165ĭi Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In this investigation, the system analyzes user feedbacks to evaluate the recommendation accuracy through metrics of precision, recall and F-score rates, while cold-start users make use the system with two MovieLens datasets as main rating reference in the recommendation system.īizer C, Lehmann L, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S (2009) Dbpedia– a crystallization point for the web of data. Knowing that user rating data processing is a large-scale problem in producing high quality recommendations, MapReduce and NoSQL environments are employed in performing efficient similarity measurement algorithms whilst maintaining rating and film datasets. In order to solve cold-start problems, Cluster-based Matrix Factorization is adopted to model user implicit ratings related to Apps usage. Among those films recommended, users can give ratings and feedback, collecting film information from linked data concurrently.
Given that mobile Apps are rapidly growing, the recommender is implemented to support web services in frontend Apps. Movie recommendation systems are important tools that suggest films with respect to users’ choices through item-based collaborative filter algorithms, and have shown positive effect on the provider’s revenue.
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