Improving recommendation lists through topic diversification

@inproceedings{Ziegler2005ImprovingRL,
  title={Improving recommendation lists through topic diversification},
  author={Cai-Nicolas Ziegler and S. McNee and J. Konstan and G. Lausen},
  booktitle={WWW '05},
  year={2005}
}
In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of… Expand
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Recommendation of More Interests Based on Collaborative Filtering
  • Qian Wu, F. Tang, +4 authors Huakang Li
  • Computer Science
  • 2012 IEEE 26th International Conference on Advanced Information Networking and Applications
  • 2012
TLDR
An sampling-based algorithm Probabilistic Top-N Selection is proposed to recommend potential interests for users, and two metrics, average predicted rating and category coverage, are proposed to assess the quality of the recommendation list. Expand
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