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sequence modeling

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Tencent Advertising Technology
Tencent Advertising Technology
Oct 17, 2024 · Artificial Intelligence

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

This article presents a comprehensive solution for heterogeneous long‑behavior sequence modeling in advertising recommendation, introducing the TIN backbone, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec, along with platform‑level optimizations that enable million‑scale sequences while delivering significant online performance gains.

Transformeradvertisingdeep learning
0 likes · 15 min read
Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec
Model Perspective
Model Perspective
Aug 15, 2022 · Artificial Intelligence

Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras

This article introduces recurrent neural networks (RNNs) and their ability to handle sequential data, explains the limitations of vanilla RNNs, presents the LSTM architecture with its gates, and provides complete Keras code for data loading, model building, and training both vanilla RNN and LSTM models.

KerasLSTMRNN
0 likes · 5 min read
Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras
DataFunTalk
DataFunTalk
Apr 28, 2022 · Artificial Intelligence

Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec

This article reviews the evolution of behavior‑sequence modeling methods—from pooling and target‑attention to RNN, capsule, transformer, and graph neural networks—explains their industrial relevance, and demonstrates how to quickly apply these techniques in the EasyRec framework with practical configuration examples.

DINEasyRecRecommendation systems
0 likes · 21 min read
Sequence Feature Modeling in Large-Scale Recommendation Systems and Fast Deployment with EasyRec
DataFunSummit
DataFunSummit
Apr 11, 2022 · Artificial Intelligence

Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning

This article presents a comprehensive overview of QQ Music's recall pipeline, detailing business characteristics, challenges such as noisy user behavior and cold‑start, and four major solutions—including knowledge‑graph‑enhanced recall, sequence‑based and multi‑interest modeling, audio‑based recall, and federated learning—along with practical insights and Q&A.

Audio EmbeddingFederated LearningRecommendation systems
0 likes · 19 min read
Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning
DataFunTalk
DataFunTalk
Apr 3, 2022 · Artificial Intelligence

Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning

This article presents a comprehensive overview of QQ Music's recall system, detailing business scenarios, challenges such as noisy user behavior and cold‑start, and four key solutions—including knowledge‑graph‑enhanced recall, sequence and multi‑interest modeling, audio‑based recall, and federated learning—along with experimental results, deployment details, and a Q&A session.

Audio EmbeddingFederated Learningknowledge graph
0 likes · 20 min read
Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning
DataFunTalk
DataFunTalk
Feb 10, 2022 · Artificial Intelligence

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

This article details the technical evolution of Kuaishou's short‑video recommendation pipeline, focusing on sequence re‑ranking, multi‑content mixing, and on‑device re‑ranking, and explains how transformer‑based models, generator‑evaluator frameworks, and reinforcement‑learning strategies are employed to maximize overall sequence value, user engagement, and revenue.

Kuaishoumulti-content mixingon-device inference
0 likes · 15 min read
Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System
58 Tech
58 Tech
Apr 12, 2021 · Artificial Intelligence

Deep Interest Modeling and Multi‑Channel Recommendation for 58.com Home Page

This article presents the challenges of large‑scale home‑page recommendation at 58.com, describes how behavior‑sequence models such as DIN, DIEN and Transformer are applied and evolved into double‑channel and multi‑channel deep interest architectures, and details offline and online performance optimizations that yielded significant gains in click‑through and conversion rates.

AITransformerdeep learning
0 likes · 19 min read
Deep Interest Modeling and Multi‑Channel Recommendation for 58.com Home Page
DataFunTalk
DataFunTalk
Apr 3, 2021 · Artificial Intelligence

A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising

User behavior sequence modeling, crucial for search and recommendation advertising ranking, has evolved from simple pooling to attention, RNN, capsule, and Transformer architectures, with industrial applications across e‑commerce, social, video, and music platforms, and future directions include time‑aware, multi‑dimensional, and self‑supervised approaches.

AttentionRecommendation systemsTransformer
0 likes · 24 min read
A Survey of User Behavior Sequence Modeling for Search and Recommendation Advertising
Qunar Tech Salon
Qunar Tech Salon
Apr 27, 2017 · Artificial Intelligence

LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling

The paper introduces LSTM‑Jump, a reinforcement‑learning‑trained LSTM variant that can dynamically skip irrelevant tokens, achieving up to six‑fold speed‑ups over standard sequential LSTMs while maintaining or improving accuracy on various NLP tasks such as sentiment analysis, document classification, and question answering.

LSTMNLPreinforcement learning
0 likes · 7 min read
LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling