diversity | BestHub

DataFunTalk

Jan 18, 2025 · Artificial Intelligence

Understanding Xiaohongshu’s Content Recommendation Mechanisms: NoteLLM and SSD

This article analyzes Xiaohongshu’s content recommendation system by reviewing two official papers, detailing the NoteLLM framework for interest discovery and the Sliding Spectrum Decomposition (SSD) method for diversified recommendations, and explaining their underlying models, loss functions, and experimental results.

LLMRecommendation systemscollaborative filtering

0 likes · 13 min read

Understanding Xiaohongshu’s Content Recommendation Mechanisms: NoteLLM and SSD

Model Perspective

Dec 3, 2024 · Artificial Intelligence

How Recommendation Algorithms Shape Our Habits—and What You Can Do About It

The article examines how recommendation algorithms reinforce user preferences, turning habits into stable feedback loops, and proposes mathematical models and practical strategies to introduce diversity and break behavioral fixation in the age of algorithmic personalization.

behavioral modelingdiversityhabit formation

0 likes · 7 min read

How Recommendation Algorithms Shape Our Habits—and What You Can Do About It

DataFunTalk

Aug 31, 2024 · Artificial Intelligence

Preference‑Oriented Diversity Model Based on Mutual Information for E‑commerce Search Re‑ranking (SIGIR 2024)

This paper, accepted at SIGIR 2024, introduces PODM‑MI, a preference‑oriented diversity re‑ranking model for e‑commerce search that jointly optimizes accuracy and diversity by modeling user intent with multivariate Gaussian distributions and maximizing mutual information between user preferences and candidate items.

diversitye-commerce searchmutual information

0 likes · 11 min read

Preference‑Oriented Diversity Model Based on Mutual Information for E‑commerce Search Re‑ranking (SIGIR 2024)

JD Tech

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)

This article presents the SIGIR 2024 accepted PODM‑MI model, which uses variational inference and mutual‑information maximization to jointly optimize relevance and diversity in JD e‑commerce search re‑ranking, demonstrating significant gains in both user conversion and result diversity through extensive online experiments.

diversitye-commerce searchmutual information

0 likes · 11 min read

JD Tech Talk

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Re-ranking (SIGIR 2024)

The paper proposes PODM‑MI, a mutual‑information‑driven, preference‑oriented diversity model that jointly optimizes accuracy and diversity in e‑commerce search re‑ranking by modeling user preferences with multivariate Gaussian distributions and adapting rankings via a learnable utility matrix, showing significant gains in JD's main search experiments.

AIdiversitye-commerce search

0 likes · 12 min read

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Re-ranking (SIGIR 2024)

JD Retail Technology

Aug 26, 2024 · Artificial Intelligence

Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)

This article introduces PODM‑MI, a preference‑oriented diversity model that uses mutual information and variational Gaussian representations to jointly optimize accuracy and diversity in e‑commerce search re‑ranking, and reports significant online A/B test improvements on JD.com.

diversitye-commercemachine learning

0 likes · 10 min read

Airbnb Technology Team

Aug 3, 2023 · Artificial Intelligence

Improving Airbnb Search Ranking Diversity with Neural Networks

Airbnb upgraded its neural‑network ranking system by adding a similarity network that penalizes duplicate‑like listings, enabling the algorithm to present a more diverse set of options, which boosted booking rates, value, and five‑star ratings, demonstrating that reduced result similarity improves overall search quality.

AirbnbNeural Networkdiversity

0 likes · 8 min read

Improving Airbnb Search Ranking Diversity with Neural Networks

NetEase LeiHuo Testing Center

Nov 4, 2022 · Game Development

Inclusive Language in Game Localization: Principles, Practices, and Multilingual Design

This article examines the importance of inclusive language in game localization, defines its principles, outlines common pitfalls such as gendered and racial terms, presents practical guidelines and multilingual strategies, and argues that adopting equitable language fosters a fairer, more welcoming gaming experience for diverse players worldwide.

Equalitydiversitygame localization

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Inclusive Language in Game Localization: Principles, Practices, and Multilingual Design

Zhuanzhuan Tech

Oct 14, 2022 · Artificial Intelligence

Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization

The article explains how recommendation systems balance exploitation and exploration, details various bias sources such as selection, exposure, conformity, and position bias, presents mitigation techniques like feature input, bias towers, and greedy algorithms, and discusses diversity‑focused exploration using DPP methods.

Bias MitigationRecommendation systemsdiversity

0 likes · 7 min read

Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization

Xiaohongshu Tech REDtech

May 18, 2022 · Artificial Intelligence

Sliding Spectrum Decomposition for Diversified Recommendation in Feed Systems

The paper introduces Sliding Spectrum Decomposition (SSD), a tensor‑based method that quantifies feed diversity through singular‑value volume within sliding windows, integrates quality‑exploration trade‑offs, and employs a hybrid CB2CF model for item embeddings, achieving superior offline and online performance versus DPP in Xiaohongshu’s feed.

Recommendation systemsdiversitymachine learning

0 likes · 10 min read

Sliding Spectrum Decomposition for Diversified Recommendation in Feed Systems

DataFunSummit

Apr 28, 2022 · Artificial Intelligence

ReRank: The Backstage of Recommendation Systems and Its Evolution Toward Ecosystem Reshaping

This article explores the role of ReRank in recommendation and advertising pipelines, detailing its algorithmic position, the challenges of diversity versus relevance, evaluation metrics such as DCG/NDCG, the evolution from heuristic methods to deep learning models, and practical insights from industry cases like Airbnb and Alibaba.

ReRankRecommendation systemsadvertising

0 likes · 57 min read

ReRank: The Backstage of Recommendation Systems and Its Evolution Toward Ecosystem Reshaping

DaTaobao Tech

Apr 19, 2022 · Artificial Intelligence

Generative Re‑ranking for Diverse and Context‑Aware Recommendation

The paper presents a generative re‑ranking framework for Taobao’s home‑decor channel that combines heuristic sequence generation methods (MMR, DPP, beam search) with a context‑aware encoder to produce diverse, relevance‑balanced recommendation lists, achieving notable gains in PV, IPV, CTR and click‑diversity over traditional point‑wise ranking.

context-awarediversitygenerative re-ranking

0 likes · 19 min read

Generative Re‑ranking for Diverse and Context‑Aware Recommendation

Tencent Cloud Developer

Apr 7, 2022 · Artificial Intelligence

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

The article surveys the re‑ranking stage of modern recommendation pipelines, detailing its architecture after recall and precise ranking, and examining how shuffling and diversity improve user experience, while multi‑task fusion, context‑aware learning‑to‑rank, real‑time online learning, and traffic‑control strategies balance accuracy, efficiency, and business responsiveness.

algorithmdiversitymachine learning

0 likes · 15 min read

Re‑ranking in Recommendation Systems: Architecture, Techniques, and Efficiency

DataFunSummit

Nov 19, 2021 · Artificial Intelligence

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

This article reviews the Sliding Spectrum Decomposition (SSD) model presented by Xiaohongshu at KDD 2021, explaining how it incorporates sliding‑window diversity into the re‑ranking stage, combines content‑based and collaborative‑filtering embeddings via the CB2CF framework, and demonstrates its effectiveness through offline and online A/B experiments.

SSDdiversityembedding

0 likes · 14 min read

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

DataFunSummit

Aug 8, 2021 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity in recommendation systems should be treated as a means rather than an ultimate goal, explains why it is hard to quantify, suggests using real performance metrics such as click‑through rate and dwell time, and offers practical strategies to improve listwise ranking.

Rankingdiversitylistwise

0 likes · 7 min read

Diversity as a Means, Not an End, in Recommendation Systems

58 Tech

Sep 7, 2020 · Artificial Intelligence

Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe

This article presents a comprehensive study on improving individual diversity in recommendation systems by detailing architectural optimizations across recall, rule, and re‑ranking layers, explaining the principles and practical deployment of MMR and DPP algorithms, and demonstrating their impact on key business metrics through extensive experiments.

Custom DistanceDPPMMR

0 likes · 18 min read

Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe

DataFunTalk

Jul 9, 2020 · Artificial Intelligence

Cross‑Domain Recommendation and Heterogeneous Mixed‑Feed Ranking Practices in 58 Community

This article presents a comprehensive overview of 58 Community's recommendation ecosystem, detailing its business background, cross‑domain recommendation concepts, three key challenges, practical solutions such as cross‑domain collaborative filtering with factorization machines, attribute‑mapping and multi‑view DSSM approaches, as well as the engineering of heterogeneous mixed‑feed ranking using scoring alignment, MMR and DPP diversity algorithms, and reports significant online performance gains.

Factorization MachinesRankingcross-domain recommendation

0 likes · 27 min read

Cross‑Domain Recommendation and Heterogeneous Mixed‑Feed Ranking Practices in 58 Community

Full-Stack Internet Architecture

Jun 8, 2020 · Fundamentals

2023 Stack Overflow Developer Survey: Key Findings on Technologies, Roles, Salaries, and Demographics

The 2023 Stack Overflow Developer Survey reveals global trends in programming language popularity, developer roles, experience levels, education, gender diversity, salary distribution, employment status, and work habits, highlighting the impact of the pandemic and the growing importance of DevOps and AI-related technologies.

DevOpsSalariesStack Overflow Survey

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2023 Stack Overflow Developer Survey: Key Findings on Technologies, Roles, Salaries, and Demographics

DataFunTalk

Sep 20, 2019 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity should be treated as a tool rather than a final objective in recommendation systems, explains why it is hard to quantify, discusses appropriate metrics such as user feedback and engagement, and presents practical strategies—including expert rules, richer recall pipelines, and list‑wise modeling—to improve diversity while optimizing true business goals.

Rankingdiversitylistwise

0 likes · 7 min read

DataFunTalk

Jul 3, 2019 · Artificial Intelligence

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization

The article explains how recommendation systems balance exploitation and exploration, introduces diversity metrics such as temporal, spatial, and coverage, and presents a determinantal point process (DPP) based algorithm accelerated by Cholesky decomposition and greedy inference, demonstrating significant speedups and improved relevance‑diversity trade‑offs in experiments.

Recommendation systemscholesky decompositiondeterminantal point process

0 likes · 10 min read

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization