Map: A model-agnostic pretraining framework for click-through rate prediction
With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …
prediction has received more and more attention and research. The most prominent features …
Contrastive self-supervised learning in recommender systems: A survey
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …
years. However, these methods usually heavily rely on labeled data (ie, user-item …
Towards deeper, lighter and interpretable cross network for ctr prediction
Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …
online advertising. It is crucial to effectively model feature interactions to improve the …
Mitigating idiom inconsistency: A multi-Semantic Contrastive Learning Method for Chinese idiom reading comprehension
Chinese idioms pose a significant challenge for machine reading comprehension due to
their metaphorical meanings often diverging from their literal counterparts, leading to …
their metaphorical meanings often diverging from their literal counterparts, leading to …
RecDCL: Dual Contrastive Learning for Recommendation
Self-supervised learning (SSL) has recently achieved great success in mining the user-item
interactions for collaborative filtering. As a major paradigm, contrastive learning (CL) based …
interactions for collaborative filtering. As a major paradigm, contrastive learning (CL) based …
Ad Recommendation in a Collapsed and Entangled World
In this paper, we present an industry ad recommendation system, paying attention to the
challenges and practices of learning appropriate representations. Our study begins by …
challenges and practices of learning appropriate representations. Our study begins by …
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Click-through rate (CTR) prediction is a vital task in industrial recommendation systems.
Most existing methods focus on the network architecture design of the CTR model for better …
Most existing methods focus on the network architecture design of the CTR model for better …
Multimodal Optimal Transport Knowledge Distillation for Cross-domain Recommendation
W Yang, J Yang, Y Liu - Proceedings of the 32nd ACM International …, 2023 - dl.acm.org
Recommendation systems have been widely used in e-commerce, news media, and short
video platforms. With the abundance of images, text, and audio information, users often …
video platforms. With the abundance of images, text, and audio information, users often …
OptMSM: Optimizing multi-scenario modeling for click-through rate prediction
A large-scale industrial recommendation platform typically consists of multiple associated
scenarios, requiring a unified click-through rate (CTR) prediction model to serve them …
scenarios, requiring a unified click-through rate (CTR) prediction model to serve them …
Anytime neural architecture search on tabular data
The increasing demand for tabular data analysis calls for transitioning from manual
architecture design to Neural Architecture Search (NAS). This transition demands an …
architecture design to Neural Architecture Search (NAS). This transition demands an …