  The emerging meta- and multi-verse landscape is yet another step towards the
more prevalent use of already ubiquitous online markets. In such markets,
recommender systems play critical roles by offering items of interest to the
users, thereby narrowing down a vast search space that comprises hundreds of
thousands of products. Recommender systems are usually designed to learn common
user behaviors and rely on them for inference. This approach, while effective,
is oblivious to subtle idiosyncrasies that differentiate humans from each
other. Focusing on this observation, we propose an architecture that relies on
common patterns as well as individual behaviors to tailor its recommendations
for each person. Simulations under a controlled environment show that our
proposed model learns interpretable personalized user behaviors. Our empirical
results on Nielsen Consumer Panel dataset indicate that the proposed approach
achieves up to 27.9% performance improvement compared to the state-of-the-art.
