Research on User-based Normalization Collaborative Filtering Recommendation Algorithm
- 10.2991/ameii-16.2016.294How to use a DOI?
- Normalization, Collaborative Filtering, Sparsity, Recommendation Algorithm
Under the circumstance of the big data, because of the low efficiency and low performance of analysis and calculation in stand-alone mode, the traditional recommendation algorithm is limited greatly, recommended time and recommended precision is difficult to guarantee. This thesis makes a improvement on the user-based collaborative filtering algorithm. The user similarity calculation is based on the normalized method, which makes user rating more reasonable and reduces the data sparse. Meanwhile the algorithm can be run on the massive clusters, which improve the operation efficiency of the system and the scalability of the system significantly. Finally, designing the scheme of recommendation system experiments and the experimental results show that the accuracy of the improved algorithm is superior to the traditional collaborative filtering algorithm and strengthen the efficiency at the same time.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jie Dong AU - Jin Li AU - Gui Li AU - Liming Du PY - 2016/04 DA - 2016/04 TI - Research on User-based Normalization Collaborative Filtering Recommendation Algorithm BT - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/ameii-16.2016.294 DO - 10.2991/ameii-16.2016.294 ID - Dong2016/04 ER -