The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

Recently, there has been a growing interest in automating the process of neural architecture
design, and the Differentiable Architecture Search (DARTS) method makes the process …

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) estimation plays as a core function module in various personalized
online services, including online advertising, recommender systems, and web search etc …

Integrated recommendation aims to jointly recommend heterogeneous items in the main
feed from different sources via multiple channels, which needs to capture user preferences …

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

Feature quality has an impactful effect on recommendation performance. Thereby, feature
selection is a critical process in developing deep learning-based recommender systems …

Deep learning-based recommender systems (DLRSs) often have embedding layers, which
are utilized to lessen the dimension of categorical variables (eg, user/item identifiers) and …

Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …