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Found 25 papers, 13 papers with code
DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air Quality Estimation
To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i. e., satellite-derived aerosol optical depth (AOD) and meteorology).
Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization.
RecDCL: Dual Contrastive Learning for Recommendation
In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation.
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.
Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms
In addition to predicting the click-through rate (CTR) or the conversion rate (CVR) as in traditional recommendations, it is essential for FinTech platforms to estimate the customers' purchase amount for each delivered fund and achieve an effective allocation of impressions based on the predicted results to optimize the total expected transaction value (ETV).
Towards Automated Negative Sampling in Implicit Recommendation
Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction
Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations.
Explicit Feature Interaction-aware Uplift Network for Online Marketing
Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i. e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario.
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works.
GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method
GTRL is the first work that incorporates the entity group modeling to capture the correlation between entities by stacking only a finite number of layers.
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Representation learning has been a critical topic in machine learning.
Optimizing Feature Set for Click-Through Rate Prediction
Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates.
Ranked #3 on
Click-Through Rate Prediction
on KDD12
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT).
SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network.
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.
Ranked #2 on
Click-Through Rate Prediction
on KDD12
DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization
Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions.
Ranked #9 on
Image Harmonization
on iHarmony4
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation
Evolving temporal networks serve as the abstractions of many real-life dynamic systems, e. g., social network and e-commerce.
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models.
Ranked #1 on
Click-Through Rate Prediction
on Avazu
Learning Cross-Domain Representation with Multi-Graph Neural Network
In fact, user's behaviors from different domains regarding the same items are usually relevant.
