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May 26, 2023 · Artificial Intelligence

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

The A2M Summit announcement details a 2023 AI and machine learning conference where senior algorithm architect Liu Lixi presents his talk on practical recommendation system techniques for sparse data, low‑frequency scenarios, and ad‑creative optimization within 58.com’s industry‑wide digital transformation.

58.comIndustrial TransformationRecommendation Systems

0 likes · 5 min read

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

Alibaba Cloud Big Data AI Platform

Mar 20, 2023 · Artificial Intelligence

How HybridBackend Supercharged Ximalaya’s Recommendation Engine with GPU Acceleration

This article details how Ximalaya’s AI Cloud adopted the open‑source HybridBackend framework to overcome sparse data access and distributed training bottlenecks, achieving multi‑GPU utilization gains, faster model training, and significant cost reductions across its recommendation services.

Distributed TrainingGPU AccelerationHybridBackend

0 likes · 9 min read

How HybridBackend Supercharged Ximalaya’s Recommendation Engine with GPU Acceleration

DataFunTalk

Sep 26, 2022 · Artificial Intelligence

Intelligent Entity Recommendation in Search Scenarios: Architecture, Relevance, Sparse Data Recall, and Multi‑Domain Strategies

This article presents a comprehensive overview of intelligent entity recommendation for search, covering scenario introduction, relevance modeling, handling sparse query and entity data with graph‑based methods, and multi‑domain, multi‑scenario ranking techniques to improve user experience.

Multi-domainSearchSparse Data

0 likes · 15 min read

Intelligent Entity Recommendation in Search Scenarios: Architecture, Relevance, Sparse Data Recall, and Multi‑Domain Strategies

DataFunSummit

Jul 11, 2022 · Artificial Intelligence

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

This article presents a comprehensive overview of conversion‑rate (CVR) optimization for Alitrip’s travel recommendation platform, detailing the challenges of extremely sparse user feedback, the design of item, user, query and context features, and a series of model‑level and loss‑function techniques—including generic‑label modeling, global‑transaction modeling, ESMM, rank‑loss approximations, and multi‑task CTR auxiliary training—to improve both CTR and CVR performance in high‑ticket‑price scenarios.

CVR optimizationRecommendation SystemsSparse Data

0 likes · 19 min read

Optimizing CVR in Sparse High‑Value Travel Recommendation Scenarios

DataFunTalk

May 14, 2022 · Artificial Intelligence

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

The 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse and Imbalanced Data (DLP‑KDD 2022) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a deadline of May 26, 2022 and acceptance notifications by June 20, 2022.

Deep LearningSparse Dataai

0 likes · 5 min read

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

DataFunSummit

Apr 15, 2021 · Artificial Intelligence

Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)

The 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a submission deadline of May 10 2021 and results announced on June 10 2021.

KDDRecommendation SystemsSparse Data

0 likes · 6 min read

Call for Papers: 3rd International Workshop on Deep Learning Practice for High-Dimensional Sparse Data (DLP‑KDD 2021)

Alibaba Cloud Developer

Aug 2, 2019 · Artificial Intelligence

Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning

This article summarizes Alibaba’s twelve KDD 2019 papers, covering advances in long‑sequence CTR modeling, fashion recommendation, sponsored search, exact‑K recommendation, meta‑learning, transfer learning, scalable graph convolution, heterogeneous graph neural networks, knowledge‑driven product description, and related workshops, highlighting both algorithmic innovations and industrial deployments.

Sparse Dataaigraph neural networks

0 likes · 20 min read

Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning

Alibaba Cloud Developer

Dec 21, 2018 · Artificial Intelligence

X-DeepLearning: Alibaba’s Open‑Source Framework for Large‑Scale Sparse Deep Learning

Alibaba's X‑DeepLearning (XDL) is an open‑source deep‑learning framework optimized for high‑dimensional sparse data, offering industrial‑grade distributed training, built‑in CTR/recommendation algorithms, structured compression, and online learning capabilities, with benchmark results demonstrating superior scalability and performance.

CTR predictionDeep LearningDistributed Training

0 likes · 18 min read

X-DeepLearning: Alibaba’s Open‑Source Framework for Large‑Scale Sparse Deep Learning

Meituan Technology Team

Mar 18, 2016 · Artificial Intelligence

Why FM and FFM Still Dominate Large‑Scale Sparse CTR Prediction

This article explains the principles of Factorization Machines (FM) and Field‑aware Factorization Machines (FFM), their implementation details, and how Meituan‑Dianping applied FFM in a DSP platform to achieve superior CTR and CVR estimation for sparse, high‑dimensional advertising data.

AdvertisingCTR predictionDSP

0 likes · 4 min read

Why FM and FFM Still Dominate Large‑Scale Sparse CTR Prediction

dbaplus Community

Jan 27, 2016 · Databases

How to Densify Sparse Data with Oracle 10g Partitioned Outer Join

This article explains why sparse data in Oracle tables hampers continuous time‑series reporting, introduces the Partitioned Outer Join syntax introduced in Oracle 10g, and demonstrates step‑by‑step how to transform one‑dimensional and multi‑dimensional gaps into dense datasets using practical SQL examples.

AnalyticsData DensificationOracle

0 likes · 17 min read

How to Densify Sparse Data with Oracle 10g Partitioned Outer Join