Item-based collaborative filtering recommendation algorithms

@inproceedings{Sarwar2001ItembasedCF,
  title={Item-based collaborative filtering recommendation algorithms},
  author={Badrul Munir Sarwar and George Karypis and Joseph A. Konstan and John Riedl},
  booktitle={WWW '01},
  year={2001}
}
Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. [...] Key Method We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the…Expand
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The experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better. Expand
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