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Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

8 years 10 months ago
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-ItemContext N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide contextaware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Rec...
Alexandros Karatzoglou, Xavier Amatriain, Linas Ba
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where RECSYS
Authors Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, Nuria Oliver
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