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Apr 23, 2026 · Artificial Intelligence

How Information Shapes Koopman Representations for World Modeling

The paper revisits Koopman representation learning through the lens of the information bottleneck, identifies the exact information needed for stable, long‑term dynamics propagation, proposes an information‑shaped objective combining mutual information and von Neumann entropy, and demonstrates superior prediction accuracy and stability across physical, visual, and graph‑based dynamical systems.

Koopmanmutual informationrepresentation learning

0 likes · 11 min read

How Information Shapes Koopman Representations for World Modeling

JD Tech

Jul 11, 2025 · Artificial Intelligence

How JD’s PODM‑MI Model Revolutionizes E‑Commerce Search Ranking

JD’s algorithm engineer recounts how his team transformed e‑commerce search by developing the PODM‑MI re‑ranking framework, uncovering a novel “hourglass” bottleneck in generative retrieval, and implementing lightweight solutions that boosted diversity, relevance, and order volume, culminating in a SIGIR publication.

Gaussian modelinge‑commercelarge-scale systems

0 likes · 8 min read

How JD’s PODM‑MI Model Revolutionizes E‑Commerce Search Ranking

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.

E-commerce SearchUser Preference ModelingVariational Inference

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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 SearchPreference Modeling

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 Cloud Developers

Aug 26, 2024 · Artificial Intelligence

Boosting E‑Commerce Re‑ranking Diversity and Accuracy with Mutual‑Information

This paper introduces PODM‑MI, a preference‑oriented diversity model that jointly optimizes relevance and diversity in e‑commerce search re‑ranking by leveraging variational inference and mutual‑information, demonstrating significant gains in both user conversion and result variety on JD.com.

DiversityE-commerce SearchPreference Modeling

0 likes · 10 min read

Boosting E‑Commerce Re‑ranking Diversity and Accuracy with Mutual‑Information

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.

DiversityPreference Modelinge‑commerce

0 likes · 10 min read

DataFunSummit

Jul 7, 2022 · Artificial Intelligence

Discovering and Enhancing Robustness in Low‑Resource Information Extraction

This article examines the robustness challenges of information extraction tasks such as NER and relation extraction, introduces the Entity Coverage Ratio metric, analyzes why pretrained models like BERT may “take shortcuts,” and proposes evaluation tools and training strategies—including mutual‑information‑based methods, negative‑training, and flooding—to improve model robustness across diverse scenarios.

BERTEvaluation MetricsRobustness

0 likes · 12 min read

Discovering and Enhancing Robustness in Low‑Resource Information Extraction

Code DAO

May 6, 2022 · Fundamentals

Information Theory Foundations for Machine Learning and Deep Learning

The article explains Shannon information content, entropy, cross‑entropy, KL‑divergence, conditional entropy and mutual information, illustrating each concept with coin‑flip and dice examples, visual formulas, and discusses their roles as loss functions and evaluation metrics in machine‑learning models.

KL divergencecross entropyentropy

0 likes · 8 min read

Information Theory Foundations for Machine Learning and Deep Learning