  Learning user representations based on historical behaviors lies at the core
of modern recommender systems. Recent advances in sequential recommenders have
convincingly demonstrated high capability in extracting effective user
representations from the given behavior sequences. Despite significant
progress, we argue that solely modeling the observational behaviors sequences
may end up with a brittle and unstable system due to the noisy and sparse
nature of user interactions logged. In this paper, we propose to learn accurate
and robust user representations, which are required to be less sensitive to
(attack on) noisy behaviors and trust more on the indispensable ones, by
modeling counterfactual data distribution. Specifically, given an observed
behavior sequence, the proposed CauseRec framework identifies dispensable and
indispensable concepts at both the fine-grained item level and the abstract
interest level. CauseRec conditionally samples user concept sequences from the
counterfactual data distributions by replacing dispensable and indispensable
concepts within the original concept sequence. With user representations
obtained from the synthesized user sequences, CauseRec performs contrastive
user representation learning by contrasting the counterfactual with the
observational. We conduct extensive experiments on real-world public
recommendation benchmarks and justify the effectiveness of CauseRec with
multi-aspects model analysis. The results demonstrate that the proposed
CauseRec outperforms state-of-the-art sequential recommenders by learning
accurate and robust user representations.
