Cold Start | BestHub

Tencent Technical Engineering

Feb 21, 2025 · Artificial Intelligence

DeepSeek-R1: Enhancing Reasoning Capabilities in LLMs via Reinforcement Learning

DeepSeek‑R1 demonstrates that large‑scale reinforcement learning, especially with the novel Group Relative Policy Optimization and a rule‑based reward scheme, can markedly boost reasoning in LLMs without heavy supervised fine‑tuning, while a brief cold‑start SFT phase, two‑stage alignment, and knowledge distillation further improve performance and efficiency, despite remaining challenges such as language mixing.

Cold StartDeepSeek-R1GRPO

0 likes · 21 min read

DeepSeek-R1: Enhancing Reasoning Capabilities in LLMs via Reinforcement Learning

DataFunSummit

Sep 16, 2024 · Artificial Intelligence

Multimodal Content Understanding and Cold-Start Practices in NetEase Cloud Music Community Recommendation System

This article details how NetEase Cloud Music leverages multimodal content understanding—using audio models like MusicCLIP and Audio MAE and image‑text fusion via FLAVA—to improve recommendation performance for new content and new users, covering system architecture, cold‑start solutions, and future AI‑driven directions.

AI modelsCold Startaudio representation

0 likes · 15 min read

Multimodal Content Understanding and Cold-Start Practices in NetEase Cloud Music Community Recommendation System

JD Tech Talk

Jun 13, 2024 · Artificial Intelligence

Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation

This article surveys how large language models can reshape recommendation systems, describes the four-stage generative pipeline, details item representation techniques such as semantic IDs, presents a JD affiliate advertising use case with offline and online experiments, and outlines future optimization directions.

Cold StartLLMgenerative recommender

0 likes · 25 min read

Generative Recommender Systems for JD Affiliate Advertising: Architecture, Methods, and Experimental Evaluation

DataFunTalk

May 12, 2024 · Artificial Intelligence

Cold Start Strategies for New Content in Baidu Feed Recommendation

This article presents Baidu's comprehensive approach to cold‑starting new content in its large‑scale feed recommendation system, covering the definition and challenges of content cold start, algorithmic practices, ID feature optimizations, traffic control mechanisms, experimental design, and key Q&A insights.

AIBaiduCold Start

0 likes · 16 min read

Cold Start Strategies for New Content in Baidu Feed Recommendation

Ximalaya Technology Team

Apr 30, 2024 · Artificial Intelligence

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

The advertising engine uses a five‑stage funnel—retrieval, recall, coarse ranking, fine ranking, and re‑ranking—each optimized with specialized indexes, multi‑channel recall, multi‑objective twin‑tower models, deep CTR/CVR predictors, and cold‑start paths, delivering up to 33 % spend growth, 6 % eCPM lift and lower latency while maintaining diversity.

Cold StartRankingadvertising

0 likes · 15 min read

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

DataFunTalk

Apr 6, 2024 · Artificial Intelligence

Exploring Large Language Models for Recommendation Systems: Experiments and Insights

This article investigates how large language models can be applied to recommendation tasks, describing two usage strategies, various ranking approaches, experimental evaluations on multiple datasets, comparisons with traditional models, and analyses of prompt design, cost, and cold‑start capabilities.

Cold StartLLMRanking

0 likes · 13 min read

Exploring Large Language Models for Recommendation Systems: Experiments and Insights

Xiaohongshu Tech REDtech

Jan 20, 2024 · Artificial Intelligence

Decoding Xiaohongshu’s Recommendation System: How Ordinary Users Gain Visibility

Xiaohongshu’s recommendation system uses large‑scale multimodal embeddings, dual‑tower and graph models, and diversity techniques like DPP and SSD to quickly surface high‑quality user‑generated content, enabling ordinary users to gain visibility while balancing personalization, exploration, and efficient LLM‑augmented pipelines.

Cold StartXiaohongshucontent diversity

0 likes · 15 min read

Decoding Xiaohongshu’s Recommendation System: How Ordinary Users Gain Visibility

DataFunSummit

Dec 8, 2023 · Artificial Intelligence

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

This article presents NetEase Cloud Music's multimodal cold‑start solution, detailing the problem background, feature selection using CLIP, two modeling approaches (I2I2U indirect and U2I DSSM direct), contrastive learning enhancements, interest‑boundary modeling, and evaluation results showing significant gains in user engagement.

AICold Startcontrastive learning

0 likes · 15 min read

Multimodal Cold‑Start Techniques for Music Recommendation at NetEase Cloud Music

DataFunSummit

Nov 24, 2023 · Artificial Intelligence

Cold-Start Content Recommendation Practices at Kuaishou

This article describes Kuaishou's approach to cold-start content recommendation, outlining the problems addressed, challenges in modeling sparse new videos, and solutions including graph neural networks, I2U retrieval, TDM hierarchical retrieval, bias correction, and future research directions.

Cold StartKuaishouRetrieval

0 likes · 19 min read

Cold-Start Content Recommendation Practices at Kuaishou

DataFunTalk

Nov 10, 2023 · Artificial Intelligence

Multimodal Cold-Start Techniques for Music Recommendation at NetEase Cloud Music

This article presents NetEase Cloud Music's multimodal cold-start recommendation approach, detailing the problem's significance, feature extraction using CLIP, I2I2U indirect modeling, U2I DSSM direct modeling with contrastive learning and interest‑boundary mechanisms, deployment pipeline, evaluation results, and future optimization directions.

Cold Startcontrastive learningdeep learning

0 likes · 14 min read

HomeTech

Nov 8, 2023 · Artificial Intelligence

Cold Start Optimization for New Content in Autohome Recommendation System

The article details how Autohome tackled the cold‑start problem for newly generated content by redesigning the recommendation pipeline, introducing multi‑path recall, refining ranking and re‑ranking formulas, and applying strategic controls, resulting in a rise of cold‑start success rate from 27% to over 99% and a CTR increase from 5% to 14%.

AICold StartRanking

0 likes · 10 min read

Cold Start Optimization for New Content in Autohome Recommendation System

Xiaohongshu Tech REDtech

Nov 6, 2023 · Artificial Intelligence

Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions

At CNCC 2023, leading researchers and industry experts convened to examine how large language models can transform recommendation systems, outlining four core challenges—model integration, fluency versus intelligence, hallucination versus deception, and user understanding—while highlighting opportunities such as multimodal content, cold‑start solutions, zero‑shot ranking, instruction‑driven algorithms, and responsible, interactive recommendation pipelines.

AICNCC 2023Cold Start

0 likes · 16 min read

Large Models and Recommendation Systems: Challenges, Opportunities, and Future Directions

DataFunSummit

Nov 1, 2023 · Artificial Intelligence

Exploring Large Language Models for Recommendation Systems: Experiments and Insights

This article investigates how large language models can be applied to recommendation tasks, presenting two usage strategies, experimental evaluations on multiple datasets, comparisons with traditional baselines, and analyses of prompting methods, cost, and cold‑start performance.

Artificial IntelligenceCold StartLLM

0 likes · 13 min read

DataFunTalk

Oct 11, 2023 · Artificial Intelligence

Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions

This article presents Kuaishou's approach to solving the content cold-start problem by analyzing its impact on video growth, detailing the challenges of sparse and biased training data, and describing a suite of graph‑neural‑network, I2U/U2I, TDM, and debiasing techniques that improve early video exposure and long‑term ecosystem health.

Cold StartI2UKuaishou

0 likes · 18 min read

Kuaishou Content Cold-Start Recommendation: Challenges, Modeling Solutions, and Future Directions

DataFunSummit

Oct 4, 2023 · Artificial Intelligence

Comprehensive Overview of Recommendation System Technologies and Their Evolution

This article provides a detailed overview of modern recommendation system technology, covering system architecture, user understanding layers, various recall and ranking techniques, additional algorithmic directions such as cold‑start and bias modeling, and the evolving evaluation metrics used in practice.

Cold StartRankingRecommendation systems

0 likes · 14 min read

Comprehensive Overview of Recommendation System Technologies and Their Evolution

DataFunSummit

Sep 29, 2023 · Artificial Intelligence

Social4Rec: Enhancing Video Recommendation with Social Interest Networks

This article introduces Social4Rec, a video recommendation algorithm that tackles user cold‑start problems by extracting and integrating social interest information through coarse‑ and fine‑grained interest extractors, attention‑based fusion, and extensive offline and online experiments demonstrating significant CTR improvements.

Cold Startattentiondeep learning

0 likes · 14 min read

Social4Rec: Enhancing Video Recommendation with Social Interest Networks

Alimama Tech

Sep 12, 2023 · Artificial Intelligence

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

CC‑GNN addresses three drawbacks of existing graph‑neural retrieval for e‑commerce by adding content phrase nodes, scalable meta‑path message passing, and difficulty‑aware noisy contrastive learning with counterfactual augmentation, achieving up to 16 % recall improvement and notably larger gains on long‑tail queries and cold‑start items.

Cold StartGraph Neural Networkscontent collaboration

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Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

Xiaohongshu Tech REDtech

Aug 25, 2023 · Artificial Intelligence

DataFunSummit 2023: Recommendation Systems Online Summit

The DataFunSummit 2023 online summit (August 26‑27) will explore eight recommendation‑system topics—including core and engineering architecture, model training/inference, large models, graphs, cold start, and multi‑task scenarios—featuring Xiaohongshu leaders who will present on graph‑based business architecture, integrated training‑inference pipelines, and user/content cold‑start strategies.

AI EngineeringCold StartRecommendation systems

0 likes · 6 min read

DataFunSummit 2023: Recommendation Systems Online Summit

JD Tech

Jul 25, 2023 · Cloud Native

Analyzing Cold‑Start Failures and Sentinel Protection in Serverless Scaling Scenarios

This article examines a real‑world case where a serverless instance’s automatic scaling was instantly overwhelmed, causing high CPU usage, frequent Full GC and JVM crashes, and then demonstrates how Sentinel’s system‑level rules can mitigate the overload and improve cold‑start performance.

CPUCold StartFullGC

0 likes · 7 min read

Analyzing Cold‑Start Failures and Sentinel Protection in Serverless Scaling Scenarios

DataFunTalk

Jun 22, 2023 · Artificial Intelligence

Social4Rec: Social Interest Enhanced Video Recommendation Algorithm

Social4Rec introduces a social interest‑enhanced video recommendation framework that tackles user cold‑start by extracting coarse‑ and fine‑grained social interests via a self‑organizing neural network and meta‑path neighborhood aggregation, integrating these embeddings with a YouTube DNN model to improve CTR and AUC.

Cold Startctrdeep learning

0 likes · 14 min read