18 citations · 34 across the 23 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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 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…
Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search
Kun-Peng Ning, Ming Pang, Zheng Fang +6
Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.…
Confidence Ranking for CTR Prediction
Jian Zhu, Congcong Liu, Pei Wang +3
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, t…
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Congcong Liu, Fei Teng, Xiwei Zhao +3
Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data…
PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving
Han Xu, Hao Qi, Kunyao Wang +8
Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking,ranking, respectively. Constrained by strict requirements on onlin…
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…
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…
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…
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…
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…
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…