Temporal interest network for click-through rate prediction
The history of user behaviors constitutes one of the most significant characteristics in
predicting the click-through rate (CTR), owing to their strong semantic and temporal …
predicting the click-through rate (CTR), owing to their strong semantic and temporal …
Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry
Y Xu, W Zhang, X Xu, B Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Real-life graphs often exhibit intricate dynamics that evolve continuously over time. To
effectively represent continuous-time dynamic graphs (CTDGs), various temporal graph …
effectively represent continuous-time dynamic graphs (CTDGs), various temporal graph …
Temporal Graph Multi-Aspect Embeddings
A Sun, Z Gong - IEEE Transactions on Knowledge and Data …, 2024 - ieeexplore.ieee.org
In recent years, graph embedding techniques have exhibited great potential for various
downstream tasks, which can leverage both topological structures and the temporal …
downstream tasks, which can leverage both topological structures and the temporal …
Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User Retention
When a customer sees a movie recommendation, she may buy the ticket right away, which is
the immediate feedback that helps improve the recommender system. Alternatively, she may …
the immediate feedback that helps improve the recommender system. Alternatively, she may …
InferTurbo: A scalable system for boosting full-graph inference of graph neural network over huge graphs
D Zhang, X Song, Z Hu, Y Li, M Tao… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
With the rapid development of Graph Neural Networks (GNNs), more and more studies focus
on system design to improve training efficiency while ignoring the efficiency of GNN …
on system design to improve training efficiency while ignoring the efficiency of GNN …
GARCIA: Powering Representations of Long-tail Query with Multi–granularity Contrastive Learning
Recently, the growth of service platforms brings great convenience to both users and
merchants, where the service search engine plays a vital role in improving the user …
merchants, where the service search engine plays a vital role in improving the user …
DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation
Abstract Dynamic Graph Neural Network (DGNN) models have been widely used for
modelling, prediction and recommendation tasks in domains such as e-commerce and …
modelling, prediction and recommendation tasks in domains such as e-commerce and …
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
Estimating individual treatment effects (ITE) from observational data is a critical task across
various domains. However, many existing works on ITE estimation overlook the influence of …
various domains. However, many existing works on ITE estimation overlook the influence of …
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Aiming at helping users locally discover retail services (eg, entertainment and dining) on
Online to Offline (O2O) service platforms, we propose COUPA, an industrial system targeting …
Online to Offline (O2O) service platforms, we propose COUPA, an industrial system targeting …