Causality-based CTR prediction using graph neural networks
As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention
from both academia and industry. Recent studies have been reported to establish CTR …
from both academia and industry. Recent studies have been reported to establish CTR …
A Dual-Embedding Based DQN for Worker Recruitment in Spatial Crowdsourcing with Social Network
Spatial Crowdsourcing (SC) is a promising service that incentives workers to finish location-
based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC …
based tasks with high quality by providing rewards. Worker recruitment is a core issue in SC …
Contextual recommendations: dynamic graph attention networks with edge adaptation
Recommender systems have witnessed a great shift in leveraging contextual information as
an auxiliary resource to improve the quality of the recommendations. These …
an auxiliary resource to improve the quality of the recommendations. These …
Graph Intention Embedding Neural Network for tag-aware recommendation
Tag-aware recommender systems leverage the vast amount of available tag records to
depict user profiles and item attributes precisely. Recently, many researchers have made …
depict user profiles and item attributes precisely. Recently, many researchers have made …
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a
longstanding problem in information retrieval. Though recent deep learning methods can …
longstanding problem in information retrieval. Though recent deep learning methods can …
IUI: Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction
M Pan, T Yu, K Zhou, Z Li, D Wang, Z Ding… - Proceedings of the …, 2023 - dl.acm.org
Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial
applications, such as recommendations and online advertising. How to precisely capture …
applications, such as recommendations and online advertising. How to precisely capture …
A novel interest evolution network based on transformer and a gated residual for ctr prediction in display advertising
C Qin, J **e, Q Jiang, X Chen - Neural Computing and Applications, 2023 - Springer
Efficiently extracting user interest from user behavior sequences is the key to improving the
click-through rate, and learning sophisticated feature interaction information is also critical in …
click-through rate, and learning sophisticated feature interaction information is also critical in …
Satisfaction-Aware User Interest Network for Click-Through Rate Prediction
M Pan, W Shi, K Zhou, Z Li, D Wang, Z Ding… - Proceedings of the …, 2023 - dl.acm.org
Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications,
including online advertising and recommender systems. Existing approaches primarily focus …
including online advertising and recommender systems. Existing approaches primarily focus …
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
Click-Through Rate (CTR) prediction is a pivotal task in product and content
recommendation, where learning effective feature embeddings is of great significance …
recommendation, where learning effective feature embeddings is of great significance …
A knowledge-enhanced interest segment division attention network for click-through rate prediction
Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a
particular item, making it one of the core tasks in various recommendation platforms. In such …
particular item, making it one of the core tasks in various recommendation platforms. In such …