Recommended Algorithm of Latent Factor Model Fused with User Clustering

DOI

10.2991/amcce-18.2018.43How to use a DOI?

Keywords

Personalized Recommendation; Latent Factor Model (LFM); User Clustering; Collaborative Filtering

Abstract

To solve the problems of partial implicit feature information loss and long-time model training caused by matrix factorization on recommended algorithm of latent factor model (LFM), a recommended algorithm of user clustering fused with latent factor model is put forward. Firstly, the users’ preference information is used to cluster them, and then the similarity calculation method is used to find the cluster and the nearest neighbor users that are most similar to the target user. Next, training similar clusters with the improved LFM to obtain the user’s implicit features Matrix p and item’s implicit feature Matrix q, and then generating the predictive score matrix of similar clusters. Finally, the predictive score of similar clusters are weighted and summed to gain the final user score. Compared with the traditional collaborative filtering and LFM, the improved model effectively reduces the training time and the root-mean-square error of predictive score, also improves the accuracy of predictive recommendation based on the experiments on Movielens datasets.

Copyright

© 2018, 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/).

Download article (PDF)

TY  - CONF
AU  - Junwei Ge
AU  - Chun Yang
AU  - Yiqiu Fang
PY  - 2018/05
DA  - 2018/05
TI  - Recommended Algorithm of Latent Factor Model Fused with User Clustering
BT  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 245
EP  - 250
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.43
DO  - 10.2991/amcce-18.2018.43
ID  - Ge2018/05
ER  -