32 citations · 59 across the 15 of their papers we have counts for
12 papers · 1 filter
DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging
Binglei Zhao, Xuanhua Yang, Xiwei Zhao +1
In industrial recommendation systems, the re-ranking stage balances business objectives and diversity for sequence-level optimization while modeling contextual information. However…
MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search
Zhen-Lin Chen, Maosen Sheng, Peng Lin +4
Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) v…
A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Binglei Zhao, Houying Qi, Guang Xu +5
Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ra…
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
Yuanbo Gao, Peng Lin, Dongyue Wang +4
Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that h…
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…
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…
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…