Service Recommendation Method Based on Collaborative Filtering and Random Forest
- Lijing Xing, Delong Ma, Bingxian Ma
- Corresponding Author
- Lijing Xing
Available Online June 2015.
- https://doi.org/10.2991/mcei-15.2015.5How to use a DOI?
- Services Recommended; Collaborative Filtering; Cross Validation Model; Random Forest Model; Multiply Users
- With the development and popularization of E-commerce, more and more information services have appeared on the web. In order to meet users requirements more accurate, several service recommendation systems had been set up. Many methods have been proposed to discover users' interest for service recommendation, such as collaborative filtering and content based service recommendation. In this paper, a new service recommendation method is proposed based on user's interest, which combines collaborative filtering based on multiply users and random forest based on single user, and this fusion method uses cross validation model. This method can improve cold start and pick up speed .Experiment results show that the method can discover users' interest efficiently and is more accurate. This method can combine two basic methods so that the result is more accurate.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lijing Xing AU - Delong Ma AU - Bingxian Ma PY - 2015/06 DA - 2015/06 TI - Service Recommendation Method Based on Collaborative Filtering and Random Forest BT - International Conference on Management, Computer and Education Informatization PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/mcei-15.2015.5 DO - https://doi.org/10.2991/mcei-15.2015.5 ID - Xing2015/06 ER -