activity
20242026
most citedDeep Situation-Aware Interaction Network for Click-Through Rate Prediction

4 citations · 4 across the 1 of their papers we have counts for

collaborators

5 papers

cs.IR20264 cited

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…

cs.IR2025

On Negative-aware Preference Optimization for Recommendation

Chenlu Ding, Daoxuan Liu, Jiancan Wu +6

Recommendation systems leverage user interaction data to suggest relevant items while filtering out irrelevant (negative) ones. The rise of large language models (LLMs) has garnere…

cs.IR2025

EGA-V1: Unifying Online Advertising with End-to-End Learning

Junyan Qiu, Ze Wang, Fan Zhang +7

Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach pr…

cs.IR2025

EGA-V2: An End-to-end Generative Framework for Industrial Advertising

Zuowu Zheng, Ze Wang, Fan Yang +4

Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and d…

cs.GT2024

Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed

Xuejian Li, Ze Wang, Bingqi Zhu +3

E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of…