@inproceedings{ea388d848ce24cff98494d8961908756,
title = "A fast and better hybrid recommender system based on spark",
abstract = "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.",
keywords = "Hybrid, Recommender system, Spark, Weight",
author = "Jiali Wang and Hang Zhuang and Changlong Li and Hang Chen and Bo Xu and Zhuocheng He and Xuehai Zhou",
note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2016.; 13th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2016 ; Conference date: 28-10-2016 Through 29-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47099-3\_12",
language = "英语",
isbn = "9783319470986",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "147--159",
editor = "Xinbo Gao and Barbara Chapman and Depei Qian and Wenguang Chen and Gao, \{Guang R.\}",
booktitle = "Network and Parallel Computing - 13th IFIP WG 10.3 International Conference, NPC 2016, Proceedings",
address = "德国",
}