194 citations · 422 across the 17 of their papers we have counts for
17 papers
Multi-Scenario Ranking with Adaptive Feature Learning
Yu Tian, Bofang Li, Si Chen +6
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mi…
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang +4
Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommenda…
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
Yujing Zhang, Zhangming Chan, Shuhao Xu +4
An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback dat…
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
Xiang-Rong Sheng, Jingyue Gao, Yueyao Cheng +6
Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibratio…
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model
Wenbo Su, Yuanxing Zhang, Yufeng Cai +9
High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly dep…
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
Rihan Chen, Bin Liu, Han Zhu +8
Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define use…
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
Kailun Wu, Zhangming Chan, Weijie Bian +5
Exploration-Exploitation (E{\&}E) algorithms are commonly adopted to deal with the feedback-loop issue in large-scale online recommender systems. Most of existing studies believe t…
Comparative Explanations of Recommendations
Aobo Yang, Nan Wang, Renqin Cai +2
As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparat…
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction
Yule Wang, Qiang Luo, Yue Ding +3
In this paper, we propose a novel model named DemiNet (short for DEpendency-Aware Multi-Interest Network) to address the above two issues. To be specific, we first consider various…
Path-based Deep Network for Candidate Item Matching in Recommenders
Houyi Li, Zhihong Chen, Chenliang Li +5
The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevan…
Learning a Product Relevance Model from Click-Through Data in E-Commerce
Shaowei Yao, Jiwei Tan, Xi Chen +4
The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential r…
Explanation as a Defense of Recommendation
Aobo Yang, Nan Wang, Hongbo Deng +1
Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanatio…
CAN: Feature Co-Action for Click-Through Rate Prediction
Weijie Bian, Kailun Wu, Lejian Ren +12
Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, De…
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Cheng Zhao, Chenliang Li, Rong Xiao +2
In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interacti…
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Zhihong Chen, Rong Xiao, Chenliang Li +3
Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and…
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Daoyuan Chen, Yaliang Li, Minghui Qiu +7
Large pre-trained language models such as BERT have shown their effectiveness in various natural language processing tasks. However, the huge parameter size makes them difficult to…
Gated Group Self-Attention for Answer Selection
Dong Xu, Jianhui Ji, Haikuan Huang +2
Answer selection (answer ranking) is one of the key steps in many kinds of question answering (QA) applications, where deep models have achieved state-of-the-art performance. Among…