WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation

@inproceedings{Chen2015WEMARECAA,
  title={WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation},
  author={Chao Chen and Dongsheng Li and Yingying Zhao and Qin Lv and Li Shang},
  booktitle={SIGIR},
  year={2015}
}
Matrix approximation is one of the most effective methods for collaborative filtering-based recommender systems. However, the high computation complexity of matrix factorization on large datasets limits its scalability. Prior solutions have adopted co-clustering methods to partition a large matrix into a set of smaller submatrices, which can then be processed in parallel to improve scalability. The drawback is that the recommendation accuracy is lower as the submatrices only contain subsets of… CONTINUE READING

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Key Quantitative Results

  • Evaluations using real-world datasets demonstrate that WEMAREC outperforms state-of-the-art matrix approximation methods in recommendation accuracy (0.5?11.9% on the MovieLens dataset and 2.2--13.1% on the Netflix dataset) with 3--10X improvement on scalability.

Citations

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