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 …

Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …

Sequential recommendation aims to model dynamic user behavior from historical
interactions. Existing methods rely on either explicit item IDs or general textual features for …

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 …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

Sequential recommendation aims to predict users' next interaction with items based on their
past engagement sequence. Recently, the advent of Large Language Models (LLMs) has …

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 …

Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …

Session-based recommendation, which aims to predict the user's immediate next action
based on anonymous sessions, is a key task in many online services (eg, e-commerce …