  Traditional recommendation models trained on observational interaction data
have generated large impacts in a wide range of applications, it faces bias
problems that cover users' true intent and thus deteriorate the recommendation
effectiveness. Existing methods tracks this problem as eliminating bias for the
robust recommendation, e.g., by re-weighting training samples or learning
disentangled representation. The disentangled representation methods as the
state-of-the-art eliminate bias through revealing cause-effect of the bias
generation. However, how to design the semantics-aware and unbiased
representation for users true intents is largely unexplored. To bridge the gap,
we are the first to propose an unbiased and semantics-aware disentanglement
learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent
Learning) from a causal perspective. Particularly, CaDSI explicitly models the
causal relations underlying recommendation task, and thus produces
semantics-aware representations via disentangling users true intents aware of
specific item context. Moreover, the causal intervention mechanism is designed
to eliminate confounding bias stemmed from context information, which further
to align the semantics-aware representation with users true intent. Extensive
experiments and case studies both validate the robustness and interpretability
of our proposed model.
