A Fast and Better Hybrid Recommender System Based on Spark - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

A Fast and Better Hybrid Recommender System Based on Spark


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.
Fichier principal
Vignette du fichier
432484_1_En_12_Chapter.pdf (661.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01648005 , version 1 (24-11-2017)





Jiali Wang, Hang Zhuang, Changlong Li, Hang Chen, Bo Xu, et al.. A Fast and Better Hybrid Recommender System Based on Spark. 13th IFIP International Conference on Network and Parallel Computing (NPC), Oct 2016, Xi'an, China. pp.147-159, ⟨10.1007/978-3-319-47099-3_12⟩. ⟨hal-01648005⟩
127 View
338 Download



Gmail Facebook X LinkedIn More