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Found 13 papers, 5 papers with code
Toward a Better Understanding of Fourier Neural Operators: Analysis and Improvement from a Spectral Perspective
In solving partial differential equations (PDEs), Fourier Neural Operators (FNOs) have exhibited notable effectiveness compared to Convolutional Neural Networks (CNNs).
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.
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.
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.
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
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.
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
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey
However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them.
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
Cross-filter compression for CNN inference acceleration
However, these filter-wise quantification methods exist a natural upper limit, caused by the size of the kernel.
