  Recommender systems that learn from implicit feedback often use large volumes
of a single type of implicit user feedback, such as clicks, to enhance the
prediction of sparse target behavior such as purchases. Using multiple types of
implicit user feedback for such target behavior prediction purposes is still an
open question. Existing studies that attempted to learn from multiple types of
user behavior often fail to: (i) learn universal and accurate user preferences
from different behavioral data distributions, and (ii) overcome the noise and
bias in observed implicit user feedback. To address the above problems, we
propose multi-behavior alignment (MBA), a novel recommendation framework that
learns from implicit feedback by using multiple types of behavioral data. We
conjecture that multiple types of behavior from the same user (e.g., clicks and
purchases) should reflect similar preferences of that user. To this end, we
regard the underlying universal user preferences as a latent variable. The
variable is inferred by maximizing the likelihood of multiple observed
behavioral data distributions and, at the same time, minimizing the
Kullback-Leibler divergence (KL-divergence) between user models learned from
auxiliary behavior (such as clicks or views) and the target behavior
separately. MBA infers universal user preferences from multi-behavior data and
performs data denoising to enable effective knowledge transfer. We conduct
experiments on three datasets, including a dataset collected from an
operational e-commerce platform. Empirical results demonstrate the
effectiveness of our proposed method in utilizing multiple types of behavioral
data to enhance the prediction of the target behavior.
