Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where
most deep CTR models follow an Embedding & Feature Interaction paradigm. However, the …

Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

Building appropriate scenarios to meet the personalized demands of different user groups is
a common practice. Despite various scenario brings personalized service, it also leads to …

Recommender Systems (RS) aim to provide personalized suggestions of items for users
against consumer over-choice. Although extensive research has been conducted to address …

Recommender systems are incremental in nature. Recent progresses in incremental
recommendation rely on capturing the temporal dynamics of users/items from temporal …

For better performance and less resource consumption, multi-scenario recommendation
(MSR) is proposed to train a unified model to serve all scenarios by leveraging data from …

Many real-world applications rely on predictive models that are incrementally learned
online. Specifically, models are updated with a single pass over continuously arriving data …

Recommender Systems (RSs) in real-world applications often deal with billions of user
interactions daily. To capture the most recent trends effectively, it is common to update the …

Graph neural networks (GNN) recently achieved huge success in collaborative filtering (CF)
due to the useful graph structure information. However, users will continuously interact with …