user behavior modeling

DataFunSummit

Mar 5, 2025 · Artificial Intelligence

Evolution and Future Trends of Recommendation Systems: From Deep Learning to Large Language Models and AI Agents

This article reviews a decade of recommendation‑system research, outlines the shift from traditional listwise methods to deep‑learning models, discusses the impact of large language models and AI agents, and presents future directions such as multimodal interaction, responsible AI, cognitive modeling, and ecosystem integration.

AI AgentsDeep LearningRecommendation systems

0 likes · 31 min read

Evolution and Future Trends of Recommendation Systems: From Deep Learning to Large Language Models and AI Agents

DeWu Technology

Feb 19, 2025 · Artificial Intelligence

Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN

The paper presents a comprehensive multi‑scenario recommendation study introducing three models—SACN, SAINet, and DSWIN—that integrate scene‑aware attention, attribute‑level preferences, and contrastive disentanglement to capture distinct user interests, achieving consistent AUC gains and online CTR improvements across real‑world datasets.

CTR predictionDeep Learningcontrastive learning

0 likes · 43 min read

Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN

AntTech

Nov 7, 2023 · Artificial Intelligence

Multi‑Scale Stochastic Distribution Prediction for User Behavior Representation Learning

The paper proposes a multi‑scale stochastic distribution prediction (MSDP) framework that learns robust user behavior representations by predicting behavior distributions over random time windows, incorporates contrastive regularization, and demonstrates superior performance on both proprietary financial risk data and a public e‑commerce dataset compared with existing masked and next‑behavior pre‑training methods.

AIdistribution predictionmulti-scale

0 likes · 13 min read

Multi‑Scale Stochastic Distribution Prediction for User Behavior Representation Learning

Kuaishou Tech

Aug 8, 2023 · Artificial Intelligence

TWIN: Two-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction

This paper presents TWIN, a two-stage interest network that aligns the similarity metrics of coarse‑grained and fine‑grained modules to improve lifelong user behavior modeling for CTR prediction in large‑scale online recommendation systems.

CTR predictionKuaishouTWIN

0 likes · 10 min read

TWIN: Two-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction

DataFunTalk

Jul 18, 2023 · Artificial Intelligence

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

This article presents Fliggy's work on user travel demand prediction, outlining the unique challenges of travel scenarios, the evolution of recall and ranking algorithms—including multi‑task learning, graph‑based models, and intention‑capture mechanisms—and discusses future research directions such as long‑sequence modeling and cross‑domain learning.

Graph Neural NetworksRecommendation systemsmachine learning

0 likes · 19 min read

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

DeWu Technology

Jul 1, 2022 · Artificial Intelligence

Multi-Objective Ranking with Deep Interest Transformer for Tabular Product Recommendation

The Dewu app’s new multi‑objective ranking model replaces the shallow ESMM baseline with a DeepFM‑based MLP and a Deep Interest Transformer that encodes up to 120 recent user actions, adds a dedicated bias network, and fuses short‑ and long‑term interests, achieving modest CTR and CVR AUC improvements while planning future tab‑specific extensions.

CVRbias netctr

0 likes · 13 min read

Multi-Objective Ranking with Deep Interest Transformer for Tabular Product Recommendation

DataFunSummit

Mar 25, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's e‑commerce recommendation system, detailing its four‑stage ranking pipeline, multi‑objective optimization with personalized fusion, transformer‑based user behavior sequence modeling, fine‑grained behavior modeling, and multimodal feature integration, and shares experimental results and engineering optimizations.

Recommendation systemse-commercemulti-objective optimization

0 likes · 17 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

DataFunTalk

Mar 14, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's end‑to‑end recommendation pipeline, detailing the four‑stage ranking chain, challenges of fine‑ranking, and practical solutions including multi‑objective learning, transformer‑based user behavior sequence modeling, fine‑grained click behavior integration, and multimodal image features, with offline and online performance gains.

Recommendation systemse-commercefine-grained behavior

0 likes · 18 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

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.

AttentionDeep LearningRecommendation systems

0 likes · 24 min read

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

DataFunTalk

Mar 20, 2021 · Artificial Intelligence

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

This article presents a comprehensive technical overview of Momo's model‑based recall system for social recommendation, detailing the underlying user‑scenario behavior models, social graph embeddings, multimodal content semantics, and deployment results that improve matching relevance and user interaction rates.

MOMORecommendation systemsembedding

0 likes · 19 min read

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

DataFunSummit

Mar 11, 2021 · Artificial Intelligence

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

This article presents the Search‑Based Interest Model (SIM), a two‑stage retrieval framework that indexes a user's entire behavior history to enable long‑term interest modeling for click‑through‑rate prediction, demonstrating practical deployment and improved recommendation of long‑term interests in e‑commerce.

AICTR predictionLong-Term Interest

0 likes · 16 min read

Search‑Based Interest Model (SIM): Long‑Term User Behavior Modeling for CTR Prediction

DataFunTalk

Nov 20, 2020 · Artificial Intelligence

NetEase Cloud Music Recommendation System: Architecture, Challenges, and AI‑Driven Solutions

The article presents a comprehensive overview of NetEase Cloud Music's recommendation system, detailing its rapid user growth, diverse music‑recommendation scenarios, differences from e‑commerce recommendation, and the evolution of recall and ranking models that leverage real‑time interest vectors, dynamic multi‑interest modeling, knowledge graphs, long‑short‑term interest mining, and multi‑path fusion to deliver personalized music experiences.

AI algorithmsKnowledge Graphinterest evolution

0 likes · 20 min read

NetEase Cloud Music Recommendation System: Architecture, Challenges, and AI‑Driven Solutions

JD Retail Technology

Oct 10, 2020 · Artificial Intelligence

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

This article introduces a Kalman Filtering Attention (KFAtt) framework that enhances click‑through‑rate (CTR) prediction by modeling user behavior with a Kalman‑filter‑based attention mechanism and a frequency‑capped variant, addressing new‑interest coverage and frequency bias in e‑commerce scenarios.

CTR predictionKalman filterattention mechanism

0 likes · 11 min read

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

Tencent Cloud Developer

Sep 3, 2020 · Artificial Intelligence

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

The paper presents an optimized CTR prediction model for Tencent’s App Store that merges multi‑behavior shared embeddings, long‑term DeepWalk graph embeddings, BERT‑derived app description vectors, and attention‑based fusion, reducing parameters while improving bias, AUC, and recommendation performance for sparse, long‑tail data.

BERTCTR predictionDeepWalk

0 likes · 9 min read

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

DataFunTalk

Aug 5, 2020 · Artificial Intelligence

EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System

The article introduces EdgeRec, an edge‑computing powered recommendation framework that moves user‑interest perception and ranking to the client side to overcome latency in traditional cloud‑centric recommender systems, detailing its architecture, heterogeneous behavior modeling, attention‑based reranking, and experimental gains.

Deep LearningRankingedge computing

0 likes · 13 min read

EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System