Algorithm optimization of recommendation based on probabilistic matrix factorization
Qi He, Yan-fen Cheng
Available Online January 2017.
- 10.2991/icmmita-16.2016.306How to use a DOI?
- Recommendation system;Probabilistic matrix factorization;User-trust network;Stochastic gradient descent.
With the rapid development of the internet, the excessive information of user and item leads to user-item rating matrix becomes bigger and more sparse, also, accuracy of the traditional collaborative filtering recommendation algorithm gets lower. So in order to improve the precision of recommendation system, this paper considered user-trust network and user rating bias had influence on the accuracy of recommendation system, designed a probabilistic matrix factorization which integrates user-trust network and user rating bias. According to the results, the proposed algorithm is superior to probabilistic matrix factorization, and has a better prediction.
- © 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 - Qi He AU - Yan-fen Cheng PY - 2017/01 DA - 2017/01 TI - Algorithm optimization of recommendation based on probabilistic matrix factorization BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 1361 EP - 1366 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-16.2016.306 DO - 10.2991/icmmita-16.2016.306 ID - He2017/01 ER -