Dynamic Item Weighting and Selection for Collaborative Filtering

@inproceedings{Baltrunas2007DynamicIW,
  title={Dynamic Item Weighting and Selection for Collaborative Filtering},
  author={Linas Baltrunas and Francesco Ricci},
  year={2007}
}
User-to-user correlation is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user correlation the importance assigned to each single item rating can be adapted by using item dependent weights. In CF, the item ratings used to make a prediction play the role of features in classical instance-based learning. This paper focuses on item weighting and item selection methods aimed at improving the recommendation accuracy by tuning the user-to-user correlation… CONTINUE READING
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