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DataFunTalk
DataFunTalk
Feb 13, 2026 · Artificial Intelligence

HyFormer: Unified Sequence Modeling and Feature Interaction for Recommendations

HyFormer, a novel hybrid Transformer framework introduced by ByteDance’s TikTok search team, integrates sequence modeling and feature interaction into a unified alternating optimization process, enhancing representation power and scaling efficiency for ultra‑long user behavior sequences and high‑dimensional heterogeneous features, leading to significant offline and online performance gains.

AIHyFormerSequence Modeling
0 likes · 12 min read
HyFormer: Unified Sequence Modeling and Feature Interaction for Recommendations
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 15, 2024 · Artificial Intelligence

Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning

Researchers from Alibaba Cloud AI and Zhejiang University present AMFormer, a Transformer‑based model that incorporates arithmetic feature interaction, demonstrating superior fine‑grained modeling, sample efficiency, and generalization on synthetic and real‑world tabular datasets, establishing a new state‑of‑the‑art in deep tabular learning.

AMFormerDeep LearningTransformer
0 likes · 12 min read
Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning
DataFunSummit
DataFunSummit
Nov 5, 2023 · Artificial Intelligence

Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach

This article presents a memory‑driven architecture (HCNet and MemoNet) that equips recommendation models with scaling‑law characteristics by storing and retrieving arbitrary feature‑combination embeddings, evaluates multi‑hash codebooks, memory‑restoring strategies, key‑feature selection, and demonstrates significant offline and online performance gains.

feature interactionlarge language modelsmemory networks
0 likes · 15 min read
Enhancing Recommendation Models with Scaling Law via HCNet and MemoNet: A Memory‑Based Feature‑Combination Approach
DataFunSummit
DataFunSummit
May 7, 2022 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training

This article reviews the development of CTR prediction models from early collaborative‑filtering methods to modern deep‑learning approaches, discusses core challenges such as feature interaction and continuous‑feature embedding, introduces recent Huawei solutions like AutoDis and ScaleFreeCTR for efficient large‑embedding training, and outlines future research directions.

Distributed TrainingEmbeddingRecommendation Systems
0 likes · 21 min read
Advances in Click‑Through Rate Prediction: Model Evolution, Feature Interaction, Continuous Feature Embedding, and Distributed Training
DataFunTalk
DataFunTalk
May 4, 2022 · Artificial Intelligence

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

This article reviews the evolution of recommendation models from early collaborative filtering to modern deep learning approaches, discusses core challenges such as CTR prediction, outlines user‑behavior and combination‑feature modeling techniques, introduces large‑embedding training and continuous‑feature embedding methods like AutoDis, and presents distributed training frameworks such as ScaleFreeCTR, concluding with future research directions.

CTR predictionDeep LearningEmbedding
0 likes · 21 min read
Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training
Alimama Tech
Alimama Tech
Mar 2, 2022 · Artificial Intelligence

Co-Action Network: A Feature Interaction Model for Click‑Through Rate Prediction

The Co‑Action Network replaces costly Cartesian‑product feature crossing with lightweight micro‑net‑based interaction units that share parameters across feature pairs, delivering comparable CTR prediction accuracy while cutting parameters to one‑tenth and boosting online latency, as proven in large‑scale advertising deployments.

Co-Action NetworkRecommendation Systemsfeature interaction
0 likes · 22 min read
Co-Action Network: A Feature Interaction Model for Click‑Through Rate Prediction
DataFunSummit
DataFunSummit
Feb 2, 2021 · Artificial Intelligence

A Comprehensive Overview of Common CTR Prediction Models and Their Evolution

This article systematically reviews the evolution of click‑through‑rate (CTR) prediction models—from early distributed linear models like logistic regression, through automated feature engineering with GBDT+LR, various factorization‑machine variants, embedding‑MLP shallow modifications, dual‑tower combinations, and advanced explicit feature‑cross networks—highlighting each model’s structure, advantages, limitations, and comparative insights.

CTR predictionclick-through ratefactorization machines
0 likes · 28 min read
A Comprehensive Overview of Common CTR Prediction Models and Their Evolution
DataFunTalk
DataFunTalk
Jan 27, 2021 · Artificial Intelligence

CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction

This article details the development and deployment of CAN (Co‑Action Net), a novel click‑through‑rate prediction model that captures item‑item co‑action via attention‑based slot embeddings, offering superior performance to Cartesian‑product methods while reducing parameter and serving costs.

CTR predictionCo-Action Netfeature interaction
0 likes · 14 min read
CAN: Revisiting Feature Co-Action for Click-Through Rate Prediction
DataFunTalk
DataFunTalk
Sep 29, 2020 · Artificial Intelligence

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

This article introduces the Deep Sparse Network (NON), a new deep neural architecture for recommendation systems that combines field‑wise networks, across‑field interaction networks, and an operation‑fusion network, and demonstrates its superior performance through extensive experiments and ablation studies.

CTR predictionDeep Learningfeature interaction
0 likes · 14 min read
Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems
Qunar Tech Salon
Qunar Tech Salon
Feb 27, 2020 · Artificial Intelligence

iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation

This article describes iQIYI’s dual‑DNN ranking architecture that combines a high‑capacity teacher network with a lightweight student network via online knowledge distillation, addressing the trade‑off between model effectiveness and inference efficiency in large‑scale recommendation systems.

CTR predictionOnline LearningRanking Models
0 likes · 12 min read
iQIYI Dual‑DNN Ranking Model with Online Knowledge Distillation
iQIYI Technical Product Team
iQIYI Technical Product Team
Feb 21, 2020 · Artificial Intelligence

Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems

iQIYI’s dual‑DNN ranking model uses an online teacher‑student knowledge‑distillation framework where a complex teacher DNN shares representations with a lightweight student DNN, enabling end‑to‑end training, large‑scale feature crossing, and substantially higher recommendation accuracy while cutting inference latency and model size.

CTR predictionOnline Learningdual DNN
0 likes · 15 min read
Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems