Papers Click-Through Rate Prediction
“Click-Through Rate Prediction” 태그가 달린 논문 407편 · 필터 해제
Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction
Generative pre-training via discrete diffusion provides dense reconstruction supervision across all feature fields simultaneously, mitigating representation collapse from data sparsity in CTR prediction. However, all exi…
Click-Through Rate PredictionSelective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR models suffer from a fundamental asymmetr…
Click-Through Rate PredictionGenerative Long-term User Interest Modeling for Click-Through Rate Prediction
Modeling long-term user interests with massive historical user behaviors enhances click-through rate (CTR) prediction performance in advertising and recommendation systems. Typically, a two-stage framework is widely adop…
Click-Through Rate PredictionRecommendation SystemsFEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While frequency-domain analysis offers a global p…
Click-Through Rate PredictionSequential RecommendationMixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long sequences: users often exhibit significa…
Click-Through Rate PredictionSequential RecommendationtensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attributes are often referred to as fields, and t…
Click-Through Rate PredictionML-DCN: Masked Low-Rank Deep Crossing Network Towards Scalable Ads Click-through Rate Prediction at Pinterest
Deep learning recommendation systems rely on feature interaction modules to model complex user-item relationships across sparse categorical and dense features. In large-scale ad ranking, increasing model capacity is a pr…
Click-Through Rate PredictionRecommendation SystemsDistribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction
This paper explores effective numerical feature embedding for Click-Through Rate prediction in streaming environments. Conventional static binning methods rely on offline statistics of numerical distributions; however, t…
Click-Through Rate PredictionGRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm
Traditional Deep Learning Recommendation Models (DLRMs) face increasing bottlenecks in performance and efficiency, often struggling with generalization and long-sequence modeling. Inspired by the scaling success of Large…
Click-Through Rate PredictionTime Aggregation Features for XGBoost Models
This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature c…
Click-Through Rate PredictionDisentangled Interest Network for Out-of-Distribution CTR Prediction
Click-through rate (CTR) prediction, which estimates the probability of a user clicking on a given item, is a critical task for online information services. Existing approaches often make strong assumptions that training…
Click-Through Rate PredictionTime Matters: A Novel Real-Time Long- and Short-term User Interest Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is a core task in online personalization platform. A key step for CTR prediction is to learn accurate user representation to capture their interests. Generally, the interest expressed …
Click-Through Rate PredictionCOINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Search
With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becomi…
Click-Through Rate PredictionDiffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to…
Click-Through Rate PredictionMulti-modal RecommendationKuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experiences. These unique characteristics introd…
Click-Through Rate PredictionMulti-Task LearningCTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as text to leverage Language Models (LMs) for…
Click-Through Rate PredictionRecommendation SystemsGenerative Click-through Rate Prediction with Applications to Search Advertising
Click-Through Rate (CTR) prediction models are integral to a myriad of industrial settings, such as personalized search advertising. Current methods typically involve feature extraction from users' historical behavior se…
Click-Through Rate PredictionPredictionGIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation
Cross-domain Click-Through Rate prediction aims to tackle the data sparsity and the cold start problems in online advertising systems by transferring knowledge from source domains to a target domain. Most existing method…
Click-Through Rate PredictionTransfer LearningAn Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify
Spotify, a large-scale multimedia platform, attracts over 675 million monthly active users who collectively consume millions of hours of music, podcasts, audiobooks, and video content. This diverse content consumption pa…
Click-Through Rate PredictionMixture-of-ExpertsMulti-Task LearningMoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors…
Click-Through Rate PredictionDiversityMixture-of-ExpertsRecommendation Systems