Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

Deep Recommender Systems (DRS) are increasingly dependent on a large number of
feature fields for more precise recommendations. Effective feature selection methods are …

As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for
data is growing. Much of this data is private and distributed across edge devices, making …

This paper investigates a top N movie recommendation system in fully automated vehicles
(FAVs). While many existing movie recommendation systems have been integrated to …

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and
advertising systems. Recent studies have shown that implementing multi-scenario …

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more
comprehensive item features and user preferences than traditional SR methods, which has …

Multi-scenario and multi-task recommendation systems efficiently facilitate knowledge
transfer across different scenarios and tasks. However, many existing approaches …

Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to
enhance performance across all recommendation scenarios, have recently gained much …

With the expansion of business scenarios, real recommender systems are facing challenges
in dealing with the constantly emerging new tasks in multi-task learning frameworks. In this …

Owing to the impressive general intelligence of large language models (LLMs), there has
been a growing trend to integrate them into recommender systems to gain a more profound …