  User behavior modeling is a key technique for recommender systems. However,
most methods focus on head users with large-scale interactions and hence suffer
from data sparsity issues. Several solutions integrate side information such as
demographic features and product reviews, another is to transfer knowledge from
other rich data sources. We argue that current methods are limited by the
strict privacy policy and have low scalability in real-world applications and
few works consider the behavioral characteristics behind long-tailed users. In
this work, we propose the Hybrid Interest Modeling (HIM) network to hybrid both
personalized interest and semi-personalized interest in learning long-tailed
users' preferences in the recommendation. To achieve this, we first design the
User Behavior Pyramid (UBP) module to capture the fine-grained personalized
interest of high confidence from sparse even noisy positive feedbacks.
Moreover, the individual interaction is too sparse and not enough for modeling
user interest adequately, we design the User Behavior Clustering (UBC) module
to learn latent user interest groups with self-supervised learning mechanism
novelly, which capture coarse-grained semi-personalized interest from
group-item interaction data. Extensive experiments on both public and
industrial datasets verify the superiority of HIM compared with the
state-of-the-art baselines.
