Improving Dynamic Recommender System Based on Item Clustering for Preference Drifts

@article{Wangwatcharakul2018ImprovingDR,
  title={Improving Dynamic Recommender System Based on Item Clustering for Preference Drifts},
  author={Charinya Wangwatcharakul and Sartra Wongthanavasu},
  journal={2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)},
  year={2018},
  pages={1-6}
}
The recommender system is an efficient tool for online application, which exploits historical user rating on item to make recommendations on items to users. This paper aims to enhance dynamic recommender systems under volatile user preference drifts. It proposed an algorithm to solve sparse data by using Gaussian mixture model to fill in data matrix for sparsity reduction and improve more completely ratings prediction. Subsequently, it utilizes item clustering and linear regression technique to… CONTINUE READING

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