5 papers
PRECTR-V2:Unified Relevance-CTR Framework with Cross-User Preference Mining, Exposure Bias Correction, and LLM-Distilled Encoder Optimization
Shuzhi Cao, Rong Chen, Ailong He +2
In search systems, effectively coordinating the two core objectives of search relevance matching and click-through rate (CTR) prediction is crucial for discovering users' interests…
DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation
Zhihao Lv, Longtao Zhang, Ailong He +3
Recommendation systems are essential for personalizing e-commerce shopping experiences. Among these, Trigger-Induced Recommendation (TIR) has emerged as a key scenario, which utili…
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
Rong Chen, Shuzhi Cao, Ailong He +2
The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for…
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Wenhao Wu, Jialiang Zhou, Ailong He +3
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only…
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
Chenglei Niu, Guojing Zhong, Ying Liu +4
With the rapid development of online advertising and recommendation systems, click-through rate prediction is expected to play an increasingly important role.Recently many DNN-base…