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Response Aware Model-Based Collaborative Filtering

Response Aware Model-Based Collaborative Filtering

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Response Aware Model-Based Collaborative Filtering

 The response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users' response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.


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  • Model: PROJ5162
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Monday 12 September, 2016.