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A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A CTR prediction model based on user interest via attention mechanism
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Efficient algorithms for ranking with SVMs
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Pre-trained models for natural language processing: A survey
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Multilayer perceptrons for classification and regression
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Collaborative filtering and deep learning based recommendation system for cold start items
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Digital servitization: Crossing the perspectives of digitization and servitization
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Click-through rate prediction in online advertising: A literature review
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Multitask Learning
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Sharing economy enabled digital platforms for development
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Multi-Task and Multi-Scene Unified Ranking Model for Online Advertising
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Predicting clicks: CTR estimation of advertisements using Logistic Regression classifier
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Factorization Machines
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Product-Based Neural Networks for User Response Prediction
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The Regression Analysis of Binary Sequences
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Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06
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Predicting clicks
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Proceedings of the 24th International Conference on World Wide Web - WWW '15
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Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
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A Convolutional Click Prediction Model
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Proceedings of the 10th ACM Conference on Recommender Systems
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Field-aware Factorization Machines for CTR Prediction
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Proceedings of the 24th ACM international conference on Multimedia
inferredcites →https://doi.org/10.1145/2964284.2964325openalex-snapshot
Deep CTR Prediction in Display Advertising
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Deep & Cross Network for Ad Click Predictions
inferredcites →https://doi.org/10.1145/3209978.3210071openalex-snapshot
Ad Click Prediction in Sequence with Long Short-Term Memory Networks
inferredcites →https://doi.org/10.1145/3209978.3210104openalex-snapshot
Entire Space Multi-Task Model
inferredcites →https://doi.org/10.1145/3219819.3219823openalex-snapshot
Deep Interest Network for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3219819.3220007openalex-snapshot
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
inferredcites →https://doi.org/10.1145/3219819.3220023openalex-snapshot
xDeepFM
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CoNet
inferredcites →https://doi.org/10.1145/3298689.3347043openalex-snapshot
FiBiNET
inferredcites →https://doi.org/10.1145/3340531.3412713openalex-snapshot
Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space
inferredcites →https://doi.org/10.1145/3357384.3357925openalex-snapshot
AutoInt
inferredcites →https://doi.org/10.1145/3383313.3412236openalex-snapshot
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
inferredcites →https://doi.org/10.1145/3459637.3481941openalex-snapshot
One Model to Serve All
inferredcites →https://doi.org/10.1145/3459637.3481948openalex-snapshot
SAR-Net
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Automatic Expert Selection for Multi-Scenario and Multi-Task Search
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DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction
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Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
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Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling
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IntTower: The Next Generation of Two-Tower Model for Pre-Ranking System
inferredcites →https://doi.org/10.1145/3511808.3557541openalex-snapshot
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3580305.3599758openalex-snapshot
A Collaborative Transfer Learning Framework for Cross-domain Recommendation
inferredcites →https://doi.org/10.1145/3580305.3599780openalex-snapshot
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction
inferredcites →https://doi.org/10.1145/3580305.3599884openalex-snapshot
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
inferredcites →https://doi.org/10.1145/3583780.3614657openalex-snapshot
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
inferredcites →https://doi.org/10.1145/3583780.3615137openalex-snapshot
HAMUR: Hyper Adapter for Multi-Domain Recommendation
inferredcites →https://doi.org/10.1145/3583780.3615469openalex-snapshot
DFFM: Domain Facilitated Feature Modeling for CTR Prediction
inferredcites →https://doi.org/10.1145/3604915.3608857openalex-snapshot
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
inferredcites →https://doi.org/10.1145/371920.372071openalex-snapshot
Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.1162/tacl_a_00349openalex-snapshot
A Primer in BERTology: What We Know About How BERT Works
inferredcites →https://doi.org/10.1609/aaai.v33i01.33015941openalex-snapshot
Deep Interest Evolution Network for Click-Through Rate Prediction
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.372openalex-snapshot
TinyBERT: Distilling BERT for Natural Language Understanding
inferredcites →https://doi.org/10.18653/v1/2022.acl-long.26openalex-snapshot
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
inferredcites →https://doi.org/10.18653/v1/d19-1openalex-snapshot
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
inferredcites →https://doi.org/10.18653/v1/d19-1018openalex-snapshot
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.18653/v1/p19-1356openalex-snapshot
What Does BERT Learn about the Structure of Language?
inferredcites →https://doi.org/10.24963/ijcai.2017/239openalex-snapshot
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
inferredcites →https://doi.org/10.24963/ijcai.2017/343openalex-snapshot
Cross-Domain Recommendation: An Embedding and Mapping Approach
inferredcites →https://doi.org/10.3389/frai.2020.00004openalex-snapshot
An Introductory Review of Deep Learning for Prediction Models With Big Data
inferredcites →https://doi.org/10.48550/arxiv.1704.05194openalex-snapshot
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
inferredcites →https://doi.org/10.48550/arxiv.2001.08361openalex-snapshot
Scaling Laws for Neural Language Models
inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 14

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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