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- Chinese Academy of SciencesCN71 papers
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8 papers · 1 filter
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction
Yimin Lv, Shuli Wang, Beihong Jin +6
User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain ric…
Unleashing the Potential of Sparse Attention on Long-term Behaviors for CTR Prediction
Weijiang Lai, Beihong Jin, Di Zhang +5
In recent years, the success of large language models (LLMs) has driven the exploration of scaling laws in recommender systems. However, models that demonstrate scaling laws are ac…
Exploring Scaling Laws of CTR Model for Online Performance Improvement
Weijiang Lai, Beihong Jin, Jiongyan Zhang +5
CTR models play a vital role in improving user experience and boosting business revenue in many online personalized services. However, current CTR models generally encounter bottle…
Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction
Weijiang Lai, Beihong Jin, Yapeng Zhang +5
CTR (Click-Through Rate) prediction, crucial for recommender systems and online advertising, etc., has been confirmed to benefit from modeling long-term user behaviors. Nonetheless…
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
Xiaoyang Chen, Kai Hui, Ben He +3
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independent…
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Xinzhou Dong, Beihong Jin, Wei Zhuo +2
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of ite…