4 citations · 7 across the 16 of their papers we have counts for
16 papers
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…
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…
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…
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…
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…
TBIN: Modeling Long Textual Behavior Data for CTR Prediction
Shuwei Chen, Xiang Li, Jian Dong +3
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations. Inspired by the recent thriving of language models (LMs), a surge of works improve predi…
Cross-Element Combinatorial Selection for Multi-Element Creative in Display Advertising
Wei Zhang, Ping Zhang, Jian Dong +5
The effectiveness of ad creatives is greatly influenced by their visual appearance. Advertising platforms can generate ad creatives with different appearances by combining creative…
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
Xiaowen Shi, Ze Wang, Yuanying Cai +6
Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and the…
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Xiaowen Shi, Fan Yang, Ze Wang +6
Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands.…
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
Jian Dong, Yisong Yu, Yapeng Zhang +6
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understandi…
Decision-Making Context Interaction Network for Click-Through Rate Prediction
Xiang Li, Shuwei Chen, Jian Dong +4
Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems. Existing methods usually model user behaviors, while ignoring the informative conte…
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
Guogang Liao, Xuejian Li, Ze Wang +6
Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to unde…
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
Ze Wang, Guogang Liao, Xiaowen Shi +6
Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that…
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks
Ze Wang, Guogang Liao, Xiaowen Shi +5
With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and…
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
Guogang Liao, Xiaowen Shi, Ze Wang +5
A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user…
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed
Guogang Liao, Ze Wang, Xiaoxu Wu +5
E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as…