e-commerce search

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

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

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 retrievallarge language models

0 likes · 21 min read

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)

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

0 likes · 19 min read

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

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 predictionadaptive feature learninge-commerce search

0 likes · 11 min read

Maria: Multi-Scenario Ranking with Adaptive Feature Learning

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 BiddingGraph Neural NetworksKDD 2022

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