Publications (22)
Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization
Qiaolei Gu, Yu Li, DingYi Zeng +6
In e-commerce advertising, selecting the most compelling combination of creative elements -- such as titles, images, and highlights -- is critical for capturing user attention and…
Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding
Congchi Yin, Qian Yu, Zhiwei Fang +2
Recent major milestones have successfully reconstructed natural language from non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram…
Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
Ming Pang, Chunyuan Yuan, Xiaoyu He +8
Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent…
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
Xin Zhao, Zhiwei Fang, Yuchen Guo +3
A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and item…
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
Chen Yang, Jin Chen, Qian Yu +10
Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for le…
On the Adaptation to Concept Drift for CTR Prediction
Congcong Liu, Yuejiang Li, Fei Teng +5
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with…
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
Xiaoxiao Xu, Chen Yang, Qian Yu +7
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alle…
IA-GCN: Interactive Graph Convolutional Network for Recommendation
Yinan Zhang, Pei Wang, Congcong Liu +7
Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn inform…
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
Xiaoxiao Xu, Zhiwei Fang, Qian Yu +7
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling brin…
Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising
Zhenhui Liu, Chunyuan Yuan, Ming Pang +7
Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements, playing a crucial role in e-commerce search advertising. The diver…
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
Carlos Carrion, Zenan Wang, Harikesh Nair +8
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps…
NDGGNET-A Node Independent Gate based Graph Neural Networks
Ye Tang, Xuesong Yang, Xinrui Liu +3
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classifica…
Dynamic Parameterized Network for CTR Prediction
Jian Zhu, Congcong Liu, Pei Wang +6
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems. Most existing CTR prediction…
GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model
Ziheng Ni, Congcong Liu, Cai Shang +10
The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centri…
Generative Click-through Rate Prediction with Applications to Search Advertising
Lingwei Kong, Lu Wang, Changping Peng +3
Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extra…
Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems
Zhiguang Yang, Lu Wang, Chun Gan +7
"Creativity is the heart and soul of advertising services". Effective creatives can create a win-win scenario: advertisers can reach target users and achieve marketing objectives m…
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction
Congcong Liu, Yuejiang Li, Jian Zhu +4
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from…
A Semi-supervised Scalable Unified Framework for E-commerce Query Classification
Chunyuan Yuan, Chong Zhang, Zheng Fang +5
Query classification, including multiple subtasks such as intent and category prediction, is vital to e-commerce applications. E-commerce queries are usually short and lack context…
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Jinbo Song, Ruoran Huang, Xinyang Wang +9
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, in…
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce
Chunyuan Yuan, Ming Pang, Zheng Fang +3
Query intent classification is an essential module for customers to find desired products on the e-commerce application quickly. Most existing query intent classification methods r…
Domain-Aware Cross-Attention for Cross-domain Recommendation
Yuhao Luo, Shiwei Ma, Mingjun Nie +4
Cross-domain recommendation (CDR) is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existin…
ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce
Zheng Fang, Donghao Xie, Ming Pang +5
Relevance modeling in e-commerce search remains challenged by semantic gaps in term-matching methods (e.g., BM25) and neural models' reliance on the scarcity of domain-specific har…