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Model Fusion

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DataFunSummit
DataFunSummit
Dec 15, 2023 · Artificial Intelligence

Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges

This article explores how large language models can be incorporated into recommender systems, discussing background challenges, specific integration points across the recommendation pipeline, practical implementation methods, experimental results, and future research directions, while highlighting industrial considerations and potential improvements.

LLMModel Fusionfeature engineering
0 likes · 20 min read
Integrating Large Language Models into Recommender Systems: Opportunities, Methods, and Challenges
360 Tech Engineering
360 Tech Engineering
Jun 25, 2023 · Artificial Intelligence

Visual Capability as a Fundamental Requirement for AGI and the SEEChat Multimodal Dialogue Model

The article reviews why visual ability is essential for artificial general intelligence, compares native multimodal and expert‑stitching integration approaches, details the architectures of models such as KOSMOS‑1, PALM‑E, Flamingo, BLIP‑2, LLAVA, miniGPT‑4, and introduces the SEEChat project that fuses CLIP vision encoders with chatGLM6B via a projection layer, presenting its training pipeline, experimental results, and future directions.

AGIModel FusionSEEChat
0 likes · 13 min read
Visual Capability as a Fundamental Requirement for AGI and the SEEChat Multimodal Dialogue Model
DeWu Technology
DeWu Technology
Dec 27, 2021 · Artificial Intelligence

Multi-Objective Modeling and Practice in DeWu Community Recommendation System

DeWu Community’s recommendation system progressed from single‑objective CTR modeling to a multi‑objective framework that combines independent models for dwell time, video completion and user interactions via score‑fusion, ranking‑learning and multi‑task architectures with shared parameters and gradient‑blocking, delivering higher engagement and retention.

Model Fusionctrmulti-task learning
0 likes · 15 min read
Multi-Objective Modeling and Practice in DeWu Community Recommendation System
iQIYI Technical Product Team
iQIYI Technical Product Team
Jun 25, 2021 · Artificial Intelligence

Multi-Objective Optimization in Short Video Recommendation at iQIYI

iQIYI improves short‑video recommendation by applying multi‑objective optimization—weighting clicks by watch duration, fusing separate click and watch‑time models, employing multi‑task learning with ESMM/MMOE and Pareto‑guided PSO hyper‑parameter search—delivering 7%+ watch‑time growth, 20%+ interaction gains, and 1.5‑3% CTR lifts while planning cross‑scene learning and further model refinements.

Model Fusionmulti-objective optimizationmulti-task learning
0 likes · 14 min read
Multi-Objective Optimization in Short Video Recommendation at iQIYI
NetEase Media Technology Team
NetEase Media Technology Team
Apr 13, 2021 · Artificial Intelligence

Applying BERT for News Timeliness Classification at NetEase

The article describes how NetEase adapts a pre‑trained BERT model to classify news articles into ultra‑short, short, or long timeliness categories by combining rule‑based strong and weak time cues, key‑sentence extraction, domain‑embedding fusion and multi‑layer semantic aggregation, achieving accurate and interpretable predictions for its platform.

Artificial IntelligenceBERTModel Fusion
0 likes · 12 min read
Applying BERT for News Timeliness Classification at NetEase
58 Tech
58 Tech
Mar 1, 2021 · Artificial Intelligence

Intelligent QABot for 58.com: Classification and Retrieval Model Exploration

This article describes how 58.com’s AI Lab built and continuously improved the QABot intelligent customer‑service system by designing classification and retrieval models, evaluating FastText, LSTM‑DSSM, BERT and a self‑developed SPTM framework, and finally fusing them to boost answer rates and user experience.

AI chatbotBERTModel Fusion
0 likes · 9 min read
Intelligent QABot for 58.com: Classification and Retrieval Model Exploration
58 Tech
58 Tech
Jan 27, 2021 · Artificial Intelligence

Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot

This article details the BangBang intelligent customer service system's overall architecture, core capabilities, knowledge‑base construction, and successive model upgrades—from FastText to TextCNN, Bi‑LSTM, and model fusion—showing how each iteration improved accuracy, recall, and F1 scores toward a stable 95% performance level.

AILSTMModel Fusion
0 likes · 12 min read
Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot
Tencent Advertising Technology
Tencent Advertising Technology
Jul 16, 2020 · Artificial Intelligence

Interview with DYG Team Member Wang He: Team Story and Model Fusion Strategies for the Competition

In this interview, DYG team member Wang He introduces his teammates, explains why they formed the team, and shares detailed model‑fusion techniques—including input variation, diverse model architectures, and training‑target differences—to boost competition scores during the final stage.

AIMachine LearningModel Fusion
0 likes · 6 min read
Interview with DYG Team Member Wang He: Team Story and Model Fusion Strategies for the Competition
Qunar Tech Salon
Qunar Tech Salon
Apr 29, 2019 · Artificial Intelligence

Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019

The article details the Travel team's award‑winning solution for the WSDM Cup 2019 fake‑news detection task, describing data analysis, preprocessing, label‑propagation augmentation, a BERT‑based baseline, a three‑stage multi‑level model‑fusion framework, experimental results, and future directions.

BERTMachine LearningModel Fusion
0 likes · 12 min read
Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 22, 2019 · Artificial Intelligence

Experience Report of the 2018 iQIYI Multimodal Video Person Identification Challenge (WitcheR Team)

The WitcheR team won the 2018 iQIYI multimodal video person identification challenge by building a fast pipeline that combined a custom face‑and‑keypoint detector, ArcFace‑trained face embeddings, scene classification, and a three‑layer MLP with several training tricks, achieving a final mAP of 88.6 % and demonstrating the value of rapid idea validation and open‑sourced code for future challenges.

Face RecognitionMLPModel Fusion
0 likes · 12 min read
Experience Report of the 2018 iQIYI Multimodal Video Person Identification Challenge (WitcheR Team)
DataFunTalk
DataFunTalk
Sep 27, 2018 · Artificial Intelligence

Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases

The article presents a comprehensive overview of how Shumei Technology leverages machine learning—including supervised, unsupervised, and reinforcement learning methods—across its credit scoring, fraud detection, advertising, and audio content moderation services, highlighting practical challenges, model fusion techniques, and future research directions.

Machine LearningModel FusionReinforcement Learning
0 likes · 12 min read
Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases
Tencent Advertising Technology
Tencent Advertising Technology
Mar 27, 2018 · Artificial Intelligence

Insights and Lessons from the First Tencent Social Advertising University Algorithm Competition

The article shares a Beijing University team's experience in the first Tencent Social Advertising algorithm contest, detailing their fourth‑place finish, best‑presentation award, and five key strategies—including business‑logic analysis, model innovation, multi‑model fusion, teamwork, and leveraging existing research—to improve conversion‑rate prediction performance.

Machine LearningModel Fusionadvertising
0 likes · 6 min read
Insights and Lessons from the First Tencent Social Advertising University Algorithm Competition
JD Retail Technology
JD Retail Technology
Dec 11, 2017 · Big Data

Data Model Component Management Platform: Functions and Practices

The presentation introduces JD's Data Model Component Management Platform, detailing its four core functions—risk control, effect evaluation, model fusion, and multi‑scenario application—while explaining how these capabilities improve model reliability, commercial value, and operational efficiency across numerous business products.

JDModel Fusionbig data
0 likes · 8 min read
Data Model Component Management Platform: Functions and Practices
Tencent Advertising Technology
Tencent Advertising Technology
Jun 23, 2017 · Artificial Intelligence

Weekly Champion nju_newbiew Shares Competition Experience and Technical Insights

The nju_newbiew team, winners of the weekly champion in Tencent Social Ads University Algorithm Competition, recount their data processing, offline validation, feature engineering, and model strategies, highlighting practical machine‑learning lessons while also providing competition announcements and contact information.

AIData ProcessingMachine Learning
0 likes · 5 min read
Weekly Champion nju_newbiew Shares Competition Experience and Technical Insights
Ctrip Technology
Ctrip Technology
Jul 29, 2016 · Artificial Intelligence

Applying Deep Learning to Sogou Mobile Search Advertising: Multi‑Model Fusion for CTR Prediction

This article presents how deep learning techniques are applied to Sogou's mobile search advertising, detailing the system architecture, feature design, multi‑model fusion strategies, engineering implementation, evaluation metrics, and future directions for improving CTR prediction performance.

CTR predictionMachine LearningModel Fusion
0 likes · 13 min read
Applying Deep Learning to Sogou Mobile Search Advertising: Multi‑Model Fusion for CTR Prediction