Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

@article{Mirbakhsh2015ImprovingTR,
  title={Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information},
  author={Nima Mirbakhsh and Charles X. Ling},
  journal={TKDD},
  year={2015},
  volume={9},
  pages={33:1-33:19}
}
Making accurate recommendations for cold-start users is a challenging yet important problem in recommendation systems. Including more information from other domains is a natural solution to improve the recommendations. However, most previous work in cross-domain recommendations has focused on improving prediction accuracy with several severe limitations. In this article, we extend our previous work on clustering-based matrix factorization in single domains into cross domains. In addition, we… CONTINUE READING
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Improving Top-N Recommendation for Cold-Start Users via Cross-Domain Information

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