Scaling Up Matrix Factorization with Cloud Computing for Collaborative Recommendation
作者： King-Teh Lee , Shih-Hao Wang , Jhih-Yuan Huang , Wei-Po Lee
DOI: 10.1109/ICSSE.2018.8520095
关键词:
摘要: Collaborative filtering (CF) is one of the most popular and efficient recommendation methods, matrix factorization considered a useful technique to implement CF-based systems. To scale up method for large datasets, many parallel computing techniques have been proposed. In this study, we present new approach with different data distribution schemes fully exploit power memory capacity cloud platform. addition, perform several sets experiments evaluate developed in environment, results show its feasibility effectiveness.
sci-hub.se 参考文章(15)
Chuan Shi, Bin Wu, Fanglin Li, Liutong Xu, Jing Shi, A fast distributed stochastic Gradient Descent algorithm for matrix factorization international conference on big data. pp. 77- 87 ,(2014)
Ankur Narang, Abhinav Srivastava, Naga Praveen Kumar Katta, Distributed scalable collaborative filtering algorithm international conference on parallel processing. pp. 353- 365 ,(2011) , 10.1007/978-3-642-23400-2_33
Efthalia Karydi, Konstantinos Margaritis, Parallel and Distributed Collaborative Filtering: A Survey ACM Computing Surveys. ,vol. 49, pp. 37- ,(2016) , 10.1145/2951952
Yue Shi, Martha Larson, Alan Hanjalic, Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges ACM Computing Surveys. ,vol. 47, pp. 3- ,(2014) , 10.1145/2556270
Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit Dhillon, Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems 2012 IEEE 12th International Conference on Data Mining. pp. 765- 774 ,(2012) , 10.1109/ICDM.2012.168
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin, A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems ACM Transactions on Intelligent Systems and Technology. ,vol. 6, pp. 2- ,(2015) , 10.1145/2668133
Ankur Narang, Raj Gupta, Anupam Joshi, Vikas K. Garg, Highly scalable parallel collaborative filtering algorithm ieee international conference on high performance computing, data, and analytics. pp. 1- 10 ,(2010) , 10.1109/HIPC.2010.5713175
Léon Bottou, Olivier Bousquet, The Tradeoffs of Large Scale Learning neural information processing systems. ,vol. 20, pp. 161- 168 ,(2007)
Boduo Li, Sandeep Tata, Yannis Sismanis, Sparkler: supporting large-scale matrix factorization extending database technology. pp. 625- 636 ,(2013) , 10.1145/2452376.2452449
Yehuda Koren, Robert Bell, Advances in Collaborative Filtering Recommender Systems Handbook. pp. 145- 186 ,(2011) , 10.1007/978-0-387-85820-3_5