Search Results for author:
Found 63 papers, 23 papers with code
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.
MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches
A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters.
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.
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
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.
Nonlinear Stability of Complex Droop Control in Converter-Based Power Systems
In previous work, the global stability of dVOC (i. e., complex droop control) has been proven by prespecifying a nominal synchronous steady state.
Complex-Frequency Synchronization of Converter-Based Power Systems
We propose the notion of complex-frequency synchronization to study the phase-amplitude coupled stability of a power system with grid-forming virtual oscillator-controlled converters.
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.
A Graph-Enhanced Click Model for Web Search
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Deep learning-based recommendation has become a widely adopted technique in various online applications.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.
Grid-Forming and Spatially Distributed Control Design of Dynamic Virtual Power Plants
We present a novel grid-forming control design approach for dynamic virtual power plants (DVPP).
Debiased Recommendation with User Feature Balancing
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks
Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).
Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation
This criterion is proved to be a sufficient stability condition for addressing the effects of the jumps and cosine damping coefficient on the system stability.
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results.
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.
Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models
The state-of-the-art deep CTR models with parallel structure (e. g., DCN) learn explicit and implicit feature interactions through independent parallel networks.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains.
Ranked #4 on
Recommendation Systems
on Amazon-Book
Cross-Batch Negative Sampling for Training Two-Tower Recommenders
The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems.
Content Filtering Enriched GNN Framework for News Recommendation
It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously.
Context-aware Reranking with Utility Maximization for Recommendation
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Collaborative filtering (CF) is a widely studied research topic in recommender systems.
Ranked #3 on
Recommendation Systems
on Yelp2018
Why Do We Click: Visual Impression-aware News Recommendation
In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.
Mode Clustering Based Dynamic Equivalent Modeling of Wind Farm for Small-Signal Stability Analysis
It is deemed that a DEM can be used to represent the whole WF to evaluate its impact on the SSS of power systems, as long as the frequency response of the DEM adequately matches that of the detailed WF model around the frequency of oscillation modes of concern.
Top-N Recommendation with Counterfactual User Preference Simulation
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.
Retrieval & Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.
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 #2 on
Click-Through Rate Prediction
on Avazu
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.
Dual Graph enhanced Embedding Neural Network for CTR Prediction
To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.
Transient Stability of Hybrid Power Systems Dominated by Different Types of Grid-Forming Devices
This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices.
Deep Learning for Click-Through Rate Estimation
In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks.
ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table
Different from the models with dense training data, the training data for CTR models is usually high-dimensional and sparse.
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.
Modeling High-order Interactions across Multi-interests for Micro-video Recommendation
Personalized recommendation system has become pervasive in various video platform.
An Embedding Learning Framework for Numerical Features in CTR Prediction
In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved.
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.
Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults
In contrast, both positive- and negative-sequence synchronizations should be of concern for inverter-based generation (IBG) under asymmetrical faults.
Ensembled CTR Prediction via Knowledge Distillation
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.
BarsCTR: Open Benchmarking for Click-Through Rate Prediction
We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.
A Practical Incremental Method to Train Deep CTR Models
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data.
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Thus, these methods fail to distinguish the target moment from plausible negative moments.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.
Ranked #17 on
Click-Through Rate Prediction
on Criteo
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
Multi-Graph Convolution Collaborative Filtering
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
Learning Cross-Domain Representation with Multi-Graph Neural Network
In fact, user's behaviors from different domains regarding the same items are usually relevant.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.
Federated Meta-Learning with Fast Convergence and Efficient Communication
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Ranked #1 on
Click-Through Rate Prediction
on Company*
