542 citations · 1.5k across the 79 of their papers we have counts for
9 papers · 1 filter
An Embedding Learning Framework for Numerical Features in CTR Prediction
Huifeng Guo, Bo Chen, Ruiming Tang +3
Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where most deep CTR models follow an Embedding \& Feature Interaction paradigm. However, the maj…
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai, Jiawei Hou, Qing Liu +6
Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and rev…
A Practical Incremental Method to Train Deep CTR Models
Yichao Wang, Huifeng Guo, Ruiming Tang +2
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data. Such batch mode training of d…
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Yishi Xu, Yingxue Zhang, Wei Guo +3
Given the convenience of collecting information through online services, recommender systems now consume large scale data and play a more important role in improving user experienc…
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
Kelong Mao, Xi Xiao, Jieming Zhu +3
Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions. A lot of rese…
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Sijin Zhou, Xinyi Dai, Haokun Chen +5
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal w…
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
Yichao Wang, Xiangyu Zhang, Zhirong Liu +4
Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the…
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Bin Liu, Chenxu Zhu, Guilin Li +6
Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manu…
Multi-Graph Convolution Collaborative Filtering
Jianing Sun, Yingxue Zhang, Chen Ma +4
Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users an…