  User behavior data in recommender systems are driven by the complex
interactions of many latent factors behind the users' decision making
processes. The factors are highly entangled, and may range from high-level ones
that govern user intentions, to low-level ones that characterize a user's
preference when executing an intention. Learning representations that uncover
and disentangle these latent factors can bring enhanced robustness,
interpretability, and controllability. However, learning such disentangled
representations from user behavior is challenging, and remains largely
neglected by the existing literature. In this paper, we present the MACRo-mIcro
Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled
representations from user behavior. Our approach achieves macro disentanglement
by inferring the high-level concepts associated with user intentions (e.g., to
buy a shirt or a cellphone), while capturing the preference of a user regarding
the different concepts separately. A micro-disentanglement regularizer,
stemming from an information-theoretic interpretation of VAEs, then forces each
dimension of the representations to independently reflect an isolated low-level
factor (e.g., the size or the color of a shirt). Empirical results show that
our approach can achieve substantial improvement over the state-of-the-art
baselines. We further demonstrate that the learned representations are
interpretable and controllable, which can potentially lead to a new paradigm
for recommendation where users are given fine-grained control over targeted
aspects of the recommendation lists.
