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This presentation is about an interesting side project of my main research in recommender systems. It is about the preliminary examination of context-aware similarities in the factorization framework.
This work is in the intersection of the following areas: (1) implicit feedback based recommendations; (2) context / context awareness; (3) item-to-item recommendations; (4) matrix / tensor factorization. The aim of this work is to examine whether context can be used to compute more accurate item similarities based on their feature vectors. Two levels of context aware similarities are introduced: (1) context is only used during training, but not for computing the similarity; (2) context is used during the training and for the similarity computations as well.
This presentation was given at the 3rd workshop on Context-awareness in Retrieval and Recommendations (CaRR 2013) in Rome.
This presentation is about an interesting side project of my main research in recommender systems. It is about the preliminary examination of context-aware similarities in the factorization framework. This work is in the intersection of the following areas: (1) implicit feedback based recommendations; (2) context / context awareness; (3) item-to-item recommendations; (4) matrix / tensor factorization. The aim of this work is to examine whether context can be used to compute more accurate item similarities based on their feature vectors. Two levels of context aware similarities are introduced: (1) context is only used during training, but not for computing the similarity; (2) context is used during the training and for the similarity computations as well. This presentation was given at the 3rd workshop on Context-awareness in Retrieval and Recommendations (CaRR 2013) in Rome.
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