%0 Conference Proceedings
%T A Fast and Better Hybrid Recommender System Based on Spark
%+ University of Science and Technology of China [Hefei] (USTC)
%A Wang, Jiali
%A Zhuang, Hang
%A Li, Changlong
%A Chen, Hang
%A Xu, Bo
%A He, Zhuocheng
%A Zhou, Xuehai
%Z Part 5: Data Processing and Big Data
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 13th IFIP International Conference on Network and Parallel Computing (NPC)
%C Xi'an, China
%Y Guang R. Gao
%Y Depei Qian
%Y Xinbo Gao
%Y Barbara Chapman
%Y Wenguang Chen
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-9966
%P 147-159
%8 2016-10-28
%D 2016
%R 10.1007/978-3-319-47099-3_12
%K Spark
%K Recommender system
%K Hybrid
%K Weight
%Z Computer Science [cs]Conference papers
%X With the rapid development of information technology, recommender systems have become critical components to solve information overload. As an important branch, weighted hybrid recommender systems are widely used in electronic commerce sites, social networks and video websites such as Amazon, Facebook and Netflix. In practice, developers typically set a weight for each recommendation algorithm by repeating experiments until obtaining better accuracy. Despite the method could improve accuracy, it overly depends on experience of developers and the improvements are poor. What worse, workload will be heavy if the number of algorithms rises. To further improve performance of recommender systems, we design an optimal hybrid recommender system on Spark. Experimental results show that the system can improve accuracy, reduce execution time and handle large-scale datasets. Accordingly, the hybrid recommender system balances accuracy and execution time.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01648005/document
%2 https://inria.hal.science/hal-01648005/file/432484_1_En_12_Chapter.pdf
%L hal-01648005
%U https://inria.hal.science/hal-01648005
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-9966