:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yang, Hao, Yuan, Jianxin, Yang, Shuai, Xu, Linhe, Yuan, Shuo, Zeng, Yifan
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Artificial Intelligence
Online Access:	https://arxiv.org/abs/2401.10934
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Open Benchmarking for Click-Through Rate Prediction
by: Zhu, Jieming, et al.
Published: (2020)

Retrieval-Oriented Knowledge for Click-Through Rate Prediction
by: Liu, Huanshuo, et al.
Published: (2024)

Generative Long-term User Interest Modeling for Click-Through Rate Prediction
by: Shao, Jiangli, et al.
Published: (2026)

Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
by: Chen, Xu, et al.
Published: (2024)

InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
by: Zeng, Zhichen, et al.
Published: (2024)

Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling
by: Zhu, Wenqiao, et al.
Published: (2025)

Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction
by: Liu, Jiahao, et al.
Published: (2026)

RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
by: Li, Yushen, et al.
Published: (2024)

GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm
by: Chen, Shaopeng, et al.
Published: (2026)

A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features
by: Wang, Hao, et al.
Published: (2024)

FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
by: Dai, Zenan, et al.
Published: (2026)

PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
by: Gao, Yuanbo, et al.
Published: (2024)

Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
by: Cui, Xiaoxi, et al.
Published: (2025)

NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction
by: Aksu, Dogukan, et al.
Published: (2024)

Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
by: Zhang, Kexin, et al.
Published: (2024)

Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
by: Zhang, Moyu, et al.
Published: (2026)

GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
by: Wang, Haowen, et al.
Published: (2024)

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
by: Lin, Jianghao, et al.
Published: (2023)

Hyperbolic Diffusion Recommender Model
by: Yuan, Meng, et al.
Published: (2025)

COINS: SemantiC Ids Enhanced COLd Item RepresentatioN for Click-through Rate Prediction in E-commerce Search
by: Zhao, Qihang, et al.
Published: (2025)

Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs
by: Zhu, Xiaofei, et al.
Published: (2026)

SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
by: Lu, Dakuan, et al.
Published: (2025)

Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
by: Chen, Xu, et al.
Published: (2023)

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction
by: Fan, Alin, et al.
Published: (2025)

Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
by: Liang, Yu, et al.
Published: (2026)

Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models
by: Song, Tianrui, et al.
Published: (2025)

Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
by: Gangopadhyay, Briti, et al.
Published: (2025)

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models
by: Shi, Yunxiao, et al.
Published: (2025)

Preference Diffusion for Recommendation
by: Liu, Shuo, et al.
Published: (2024)

Asymmetric Diffusion Recommendation Model
by: Zhu, Yongchun, et al.
Published: (2025)

Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
by: Hou, Ruijie, et al.
Published: (2023)

Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support
by: Xu, Jing, et al.
Published: (2026)

Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research
by: Abbasi, Amirereza, et al.
Published: (2026)

Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval
by: Kim, Hyunkyu, et al.
Published: (2025)

Non-autoregressive Generative Models for Reranking Recommendation
by: Ren, Yuxin, et al.
Published: (2024)

HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations
by: Yang, Peiru, et al.
Published: (2025)

Evaluating Embedding Models and Pipeline Optimization for AI Search Quality
by: Zhong, Philip, et al.
Published: (2025)

A Novel Mamba-based Sequential Recommendation Method
by: Yuan, Jun
Published: (2025)

Spark: A System for Scientifically Creative Idea Generation
by: Sanyal, Aishik, et al.
Published: (2025)

DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
by: Li, Qinyao, et al.
Published: (2025)