Multiverse Recommendation: N-dimensional Tensor Factorization for context-aware Collaborative Filtering

Abstract

Context has been recognized as an important factor to con- sider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Ma- trix 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 Factoriza- tion that allows for a exible and generic integration of con- textual information by modeling the data as a User-Item- Context 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 context- aware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommenda- tion improves upon non-contextual Matrix Factorization up to 30% in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware meth- ods and show that Tensor Factorization consistently out- performs them both in semi-synthetic and real-world data - improvements range from 2:5% to more than 12% depend- ing on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual in- formation is available. Copyright 2010 ACM.