  Sequential recommendation requires understanding the dynamic patterns of
users' behaviors, contexts, and preferences from their historical interactions.
Most existing works focus on modeling user-item interactions only from the item
level, ignoring that they are driven by latent shopping intentions (e.g.,
ballpoint pens, miniatures, etc). The detection of the underlying shopping
intentions of users based on their historical interactions is a crucial aspect
for e-commerce platforms, such as Amazon, to enhance the convenience and
efficiency of their customers' shopping experiences. Despite its significance,
the area of main shopping intention detection remains under-investigated in the
academic literature. To fill this gap, we propose a graph-regularized
stochastic Transformer method, G-STO. By considering intentions as sets of
products and user preferences as compositions of intentions, we model both of
them as stochastic Gaussian embeddings in the latent representation space.
Instead of training the stochastic representations from scratch, we develop a
global intention relational graph as prior knowledge for regularization,
allowing relevant shopping intentions to be distributionally close. Finally, we
feed the newly regularized stochastic embeddings into Transformer-based models
to encode sequential information from the intention transitions. We evaluate
our main shopping intention identification model on three different real-world
datasets, where G-STO achieves significantly superior performances to the
baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10 on average.
