Improving Recommendation Performance with Clustering and Missing Value Prediction
2020 54th Annual Conference on Information Sciences and Systems (CISS)(2020)
Abstract
High percentage of missing values in rating matrix results in low recommendation performance, which includes poor recommendation accuracy and slow recommendation generation. This paper proposes an integrated method with clustering algorithm and missing value prediction in a Collaborative Filtering Recommendation system to address the missing value issue in the rating matrix. The proposed method is validated using the MovieLens dataset. Experimental results show that the proposed method improves recommendation quality and online scalability while reducing recommendation generation time.
MoreTranslated text
Key words
Recommendation System,Missing Value Prediction,Clustering
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined