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DataFunSummit
DataFunSummit
Nov 20, 2025 · Artificial Intelligence

How 1688 Reinvented E‑commerce Search with AI‑Powered Generative Retrieval

This article details Alibaba’s 1688 platform’s shift from traditional e‑commerce search to AI‑driven generative retrieval, covering the AI Deep Search 1.0 and 2.0 cascaded frameworks, multimodal capabilities, an end‑to‑end “model‑as‑search‑engine” approach, experimental results, challenges, and future directions.

AIE-commerce SearchGenerative Retrieval
0 likes · 18 min read
How 1688 Reinvented E‑commerce Search with AI‑Powered Generative Retrieval
JD Tech
JD Tech
Dec 14, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches

This article presents a comprehensive study of generative retrieval for large‑scale e‑commerce search, comparing lexical‑based and Semantic‑ID‑based methods, introducing a Query‑to‑MultiSpan framework, analyzing the sand‑glass distribution problem in residual quantization, and proposing heuristic and adaptive solutions to improve recall and efficiency.

AIE-commerce SearchGenerative Retrieval
0 likes · 20 min read
Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches
JD Retail Technology
JD Retail Technology
Dec 9, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches

This article presents a comprehensive study of generative retrieval in large‑scale e‑commerce search, detailing lexical‑based and SemanticID‑based methods, their challenges such as long‑tail distribution and token length, experimental evaluations, the discovered "sandglass" effect, and proposed solutions to improve recall and efficiency.

AIE-commerce SearchGenerative Retrieval
0 likes · 20 min read
Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches
DataFunSummit
DataFunSummit
Nov 28, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical and SemanticID Approaches

This article presents a comprehensive study of generative retrieval for large‑scale e‑commerce search, detailing background challenges, the advantages of generative methods, two concrete strategies—Lexical‑based and SemanticID‑based—along with task redesign, preference optimization, constrained beam search, extensive experiments, and future research directions.

E-commerce SearchGenerative RetrievalPreference Optimization
0 likes · 21 min read
Generative Retrieval for E‑commerce Search: Lexical and SemanticID Approaches
DataFunTalk
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
0 likes · 11 min read
Preference‑Oriented Diversity Model Based on Mutual Information for E‑commerce Search Re‑ranking (SIGIR 2024)
JD Tech
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
Preference-oriented Diversity Model Based on Mutual Information for E-commerce Search Re-ranking (SIGIR 2024)
JD Tech Talk
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
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
Alimama Tech
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.

E-commerce SearchLong Tailcold start
0 likes · 19 min read
Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search
Alimama Tech
Alimama Tech
Jul 5, 2023 · Artificial Intelligence

Maria: Multi-Scenario Ranking with Adaptive Feature Learning

Maria is a multi‑scenario ranking framework that adaptively learns features across heterogeneous e‑commerce query types—visual search, similar‑product search, and interest search—by employing Feature Scaling, Feature Refinement, and Feature Correlation Modeling modules, achieving superior performance and reducing the seesaw effect on the Ali‑CCP and Alimama datasets.

CTR predictionE-commerce Searchadaptive feature learning
0 likes · 11 min read
Maria: Multi-Scenario Ranking with Adaptive Feature Learning
Alimama Tech
Alimama Tech
May 23, 2022 · Artificial Intelligence

Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences

The Alibaba Mama technical team secured five paper acceptances at the prestigious KDD 2022 conference, presenting advances such as curriculum‑guided Bayesian reinforcement learning for ROI‑constrained bidding, adversarial‑gradient driven exploration for click‑through‑rate prediction, externality‑aware transformers for e‑commerce ads, multi‑modal multi‑query pretraining, and generative‑replay streaming graph neural networks.

Advertising BiddingE-commerce SearchKDD 2022
0 likes · 10 min read
Alibaba Mama Team Papers Accepted at KDD 2022 and Other Top Conferences