Multiverse recommendation

TLDR

Context is recognized as important in personalized recommender systems, yet most model‑based collaborative filtering methods such as matrix factorization lack straightforward ways to incorporate it. This work introduces a tensor‑factorization‑based collaborative filtering method that flexibly integrates contextual information by modeling data as a User‑Item‑Context tensor. The Multiverse Recommendation model treats various context types as additional tensor dimensions, extending the data representation beyond a 2D user‑item matrix. Tensor factorization produces a compact model that enables context‑aware recommendations.

Abstract

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-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.