Improved Recommendation Sorting of Collaborative Filtering Algorithm
Kaiji Liao, Nannan Sun, Jiewen OuYang
Available Online March 2017.
- 10.2991/amcce-17.2017.36How to use a DOI?
- collaborative filtering, user preferences, recommendation sorting, precision, recall rate, F1 indicators
The traditional collaborative filtering algorithm has no overall quantitative understanding on users' preference. This paper proposes a collaborative filtering algorithm based on improved recommendation sorting. Based on the traditional collaborative filtering rating prediction, three kinds of weighted sorting strategies are proposed to recommendation list, which are based on the combination of users' preference vector and item quality. Experiments on the MovieLens data set show that, in the same rating prediction process, the recommended results of the improved sorting have a significant increase of nearly one time in the precision, recall and F1 indicators.
- © 2017, 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 - Kaiji Liao AU - Nannan Sun AU - Jiewen OuYang PY - 2017/03 DA - 2017/03 TI - Improved Recommendation Sorting of Collaborative Filtering Algorithm BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 208 EP - 214 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.36 DO - 10.2991/amcce-17.2017.36 ID - Liao2017/03 ER -