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...

• Type: Conference
• Year: 2010
• Where: RECSYS
• Authors: Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, Nuria Oliver
• Keywords: Control Systems, Factorization, Matrix Factorization, RECSYS 2010, Tensor