Research of User-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop
- Y.L. Zhang, M.M Ma, S.P Wang
- Corresponding Author
- Y.L. Zhang
Available Online June 2015.
- https://doi.org/10.2991/cisia-15.2015.17How to use a DOI?
- collaborative filtering; similarity measure; recommended system; hadoop
- Collaborative filtering algorithm is one of the key technologies of the current e-commerce recommendation system, in which the effect of similarity measure directly determines the accuracy of the recommendation system. An improved method of similarity measure and the corresponding collaborative filtering recommendation algorithm by introducing a common user items popularity rating between features and user relevance is proposed in this paper. Furthermore, the implementation of collaborative filtering recommendation system based on hadoop is discussed. The respective evaluation of traditional collaborative filtering recommendation algorithm and improved recommendation algorithm by using MAE show that the improved algorithm enhances the recommendation accuracy in a certain extent. Meanwhile, the overall performance experiments show that collaborative filtering recommendation engines continue to reduce the calculation time with the appropriate increase of the virtual machine node.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Y.L. Zhang AU - M.M Ma AU - S.P Wang PY - 2015/06 DA - 2015/06 TI - Research of User-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop BT - International Conference on Computer Information Systems and Industrial Applications PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/cisia-15.2015.17 DO - https://doi.org/10.2991/cisia-15.2015.17 ID - Zhang2015/06 ER -