Recommended Algorithm of Latent Factor Model Fused with User Clustering
Junwei Ge, Chun Yang, Yiqiu Fang
Available Online May 2018.
- https://doi.org/10.2991/amcce-18.2018.43How to use a DOI?
- Personalized Recommendation; Latent Factor Model (LFM); User Clustering; Collaborative Filtering
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/amcce-18.2018.43 DO - https://doi.org/10.2991/amcce-18.2018.43 ID - Ge2018/05 ER -