Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

Modeling users' dynamic preferences from their historical behaviors is challenging and
crucial for recommendation systems. Previous methods employ sequential neural networks …

In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

To address the sparsity and cold start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …