This presentation details Shopee's approach to constructing a multilingual product knowledge graph, covering ontology modeling, data acquisition, fusion techniques, and practical applications, while discussing challenges, model architectures, and future directions for large‑scale e‑commerce AI systems.
This article presents the JD Retail search data‑science team's practical approaches to e‑commerce search, covering the scene’s unique data characteristics, order attribution methods, AB experiment design, causal‑inference frameworks, variance‑reduction techniques, quasi‑experimental evaluations, and metric design for traffic distribution, all illustrated with real‑world examples and visualizations.
This article presents Xiaomi’s e‑commerce recommendation research, addressing four key challenges—sample selection bias, data sparsity, delayed feedback, and knowledge inconsistency—by introducing the Entire Space Delayed Feedback with Cross‑Task Knowledge Distillation (ESDC) model, which combines causal inference, cross‑task distillation, twin networks, and uncertainty weighting to improve CVR prediction and achieve a 15% GMV lift over the baseline.
This article provides an overview of search, recommendation, and advertising as core internet services, comparing their problem definitions, business goals, algorithmic models, and system architectures across web, e‑commerce, and O2O scenarios, while outlining historical development and key industry examples.
This article presents a comprehensive overview of the challenges in e‑commerce product copy generation, introduces a unified framework comprising a copy generation system, a copy‑cleaning subsystem, and a quality evaluation module, and details practical optimization techniques applied to short and long copy scenarios.
This article explains how JD's intelligent recommendation selling point system leverages NLP, BERT, Transformer and pointer‑generator models to automatically create short, personalized product highlights, describing the technical background, system architecture, model training pipeline, online/offline monitoring, and the resulting business benefits.
The article presents JD's end‑to‑end recommendation pipeline, detailing the four‑stage ranking chain, challenges of fine‑ranking, and practical solutions including multi‑objective learning, transformer‑based user behavior sequence modeling, fine‑grained click behavior integration, and multimodal image features, with offline and online performance gains.
This article describes how eBay applies graph neural networks to real‑time payment fraud detection, covering the anti‑fraud scenario, limitations of traditional GBDT pipelines, challenges of constructing and serving dynamic heterogeneous graphs, the end‑to‑end solution with directed slice graphs and a Lambda‑style architecture, and experimental results comparing GNN with LightGBM.
The article presents ZEUS, a self‑supervised multi‑scenario ranking model that leverages user‑initiated behavior pre‑training to break feedback loops and improve query recommendation efficiency across diverse e‑commerce search scenarios, achieving significant gains in CTR, CVR, and GMV.
This article presents a comprehensive overview of building and applying product knowledge graphs for e‑commerce, covering background, recent advances in graph neural network‑based entity alignment, online prediction pipelines, data construction, evaluation metrics, attribute extraction, and future research directions.
This talk presents DAMO Academy’s end‑to‑end NLG pipeline for e‑commerce, detailing the XGeneration content‑reproduction system, controllable short‑text generation models (PKM and PSCN), knowledge‑graph‑to‑text generation, metaphor generation, and their extensions to short‑video script creation and intelligent video editing, together with performance results and practical applications.
This article presents a comprehensive technical overview of JD.com’s e‑commerce recommendation and advertising systems, covering business scenarios, recall optimizations (profile and similarity‑based), multi‑task ranking improvements, sample weighting, multi‑model ensembles, PID‑based CPC control, conversion‑delay modeling, and the achieved performance gains and future research plans.
This article presents a comprehensive overview of Alibaba's recommendation algorithm practice for the Feizhu travel platform, covering the evolution of e‑commerce recommendation frameworks, full‑space CVR estimation models such as ESMM, ESM² and HM³, and the specific challenges and solutions for travel‑centric recommendation scenarios.
This article presents a multi‑category Mixture‑of‑Experts (MoE) approach for e‑commerce search ranking, addressing category‑specific behavior and small‑category learning by introducing hierarchical soft constraints and adversarial regularization, and demonstrates significant AUC and NDCG gains on Amazon and JD in‑house datasets.
This article presents a comprehensive overview of sentiment analysis in user‑generated content, detailing document‑, sentence‑, and aspect‑level tasks, defining the Aspect Sentiment Triplet Extraction problem for e‑commerce reviews, describing a three‑stage pipeline with pre‑training, multi‑domain modeling and attribute normalization, and reporting significant business improvements such as 400% CTR lift, while also discussing data imbalance, annotation scarcity, and future research directions.
This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.
This article introduces the fundamentals of user profiling, explains tag types and their business value, and demonstrates a data‑driven e‑commerce case study that analyzes gender, age, region, marital status, education, profession, product preferences, purchase timing, and price sensitivity to guide targeted promotion strategies.
This article provides a comprehensive overview of e‑commerce recommendation systems, detailing their end‑to‑end workflow, key challenges such as multi‑scenario objectives and data loops, core components like recall and ranking, model evolution, feature engineering, evaluation metrics, and practical considerations for building a healthy, multi‑objective recommendation ecosystem.
This article explores the evolution of e‑commerce search, detailing why search matters, the technical pipeline—including query preprocessing, entity and intent recognition, knowledge‑graph construction, recall, coarse and fine ranking—and demonstrates substantial performance gains through real‑world case studies.
The article presents AliCoCo, Alibaba’s large‑scale e‑commerce knowledge graph that models user demand as concepts, describes its four‑layer architecture, the algorithms for concept extraction, taxonomy building, and item association, and demonstrates its impact on search and recommendation systems.