Causal Representation Learning for Out-of-Distribution Recommendation

Modern recommender systems learn user representations from historical interactions, which suffer from the problem of user feature shifts, such as an income increase. Historical interactions will inject out-of-date information into the representation in conflict with the latest user feature, leading to improper recommendations. In this work, we consider the Out-Of-Distribution (OOD) recommendation problem in an OOD environment with user feature shifts. To pursue high fidelity, we set additional objectives for representation learning as: 1) strong OOD generalization and 2) fast OOD adaptation.