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Mar 23, 2026 · Industry Insights

Why Tencent’s $885K KDD Cup Challenge Could Redefine Recommendation Systems

The 2026 KDD Cup, powered by Tencent’s Advertising Algorithm Competition with an $885,000 prize pool, challenges participants to unify sequence modeling and feature interaction in large‑scale recommendation systems, offering academic publication paths, real‑world deployment opportunities, and strict latency constraints that push both research and engineering innovation.

AIKDD CupTencent

0 likes · 16 min read

Why Tencent’s $885K KDD Cup Challenge Could Redefine Recommendation Systems

Meituan Technology Team

Aug 8, 2024 · Artificial Intelligence

BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions

The BlackPearl team from Meituan’s Dazhong Dianping division swept all three KDD 2024 OAG‑Challenge Cup tracks—WhoIsWho, PST, and AQA—by deploying innovative large‑model techniques such as iterative text clustering, graft‑learning‑enhanced BERT RAG pipelines, and a Boosting LLM‑for‑Vector search, and have released the code publicly on GitHub.

Academic DisambiguationKDD CupPaper Retrieval

0 likes · 4 min read

BlackPearl Team Wins All Three Tracks of KDD 2024 OAG‑Challenge Cup with Large‑Model Solutions

Baobao Algorithm Notes

Mar 18, 2024 · Industry Insights

Inside the 2024 KDD Cup ShopBench Challenge: Tasks, Data, and Evaluation Metrics

The 2024 KDD Cup introduces the ShopBench benchmark, a large‑scale LLM competition that simulates real‑world online shopping with 57 tasks, over 20,000 questions, and multiple tracks covering concept understanding, knowledge reasoning, user‑behavior alignment, multilingual ability, and an all‑round track, all evaluated with task‑specific metrics and a hidden test set.

DatasetEvaluation MetricsKDD Cup

0 likes · 11 min read

Inside the 2024 KDD Cup ShopBench Challenge: Tasks, Data, and Evaluation Metrics

AntTech

Sep 9, 2022 · Artificial Intelligence

Ant Security Lab Wins Two Golds and One Silver at KDD Cup 2022 with Advanced Keyword Extraction and Self‑Distillation for Product Search

Ant Security Lab's algorithm engineer Lin Jinzheng secured two gold medals and one silver at the KDD Cup 2022, ranking first globally, by applying innovative keyword‑extraction and self‑distillation techniques to improve product search relevance and interactive risk‑control systems.

AIKDD CupProduct Search

0 likes · 4 min read

Ant Security Lab Wins Two Golds and One Silver at KDD Cup 2022 with Advanced Keyword Extraction and Self‑Distillation for Product Search

Meituan Technology Team

Jan 6, 2022 · Artificial Intelligence

Multi-domain Modeling and AutoML Techniques from Kaggle/KDD Cup Championships

Drawing on seven Kaggle and KDD Cup victories, the article outlines a multi‑domain modeling optimization strategy—covering recommendation, time‑series, and AutoML problems—alongside a three‑module AutoML pipeline and a three‑stage workflow that emphasize systematic evaluation, bias‑variance balance, and robust model‑fusion for competition and industry success.

AutoMLKDD CupKaggle

0 likes · 37 min read

Multi-domain Modeling and AutoML Techniques from Kaggle/KDD Cup Championships

Meituan Technology Team

Sep 24, 2020 · Artificial Intelligence

Meituan Search Ads Team's Solution for KDD Cup 2020 Multimodalities Recall Track

Meituan’s Search Ads team placed third in the KDD Cup 2020 Multimodalities Recall track by tackling training‑test distribution mismatch with diversified negative sampling and distillation learning, and improving text‑image matching via gated fully‑connected layers, bidirectional attention, and diversified fusion, then ensembling neural and tree models for strong NDCG gains later applied to their ad creative‑selection system.

DistillationKDD CupMultimodal Learning

0 likes · 19 min read

Meituan Search Ads Team's Solution for KDD Cup 2020 Multimodalities Recall Track

Meituan Technology Team

Aug 27, 2020 · Artificial Intelligence

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

The team built an automated graph learning framework that preprocesses diverse graphs, employs four GNN architectures, conducts rapid hyper‑parameter tuning, and fuses models with density‑aware weighting, securing first place in KDD Cup 2020 AutoGraph and boosting Meituan’s ad recall and CTR prediction.

AutoMLKDD Cupgraph neural networks

0 likes · 30 min read

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

Meituan Technology Team

Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingGraph ModelingKDD Cup

0 likes · 23 min read

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

Meituan Technology Team

Sep 7, 2017 · Artificial Intelligence

Interview with Meituan’s Data Science Expert Yan Peng on KDD Cup Success and Machine Learning Insights

In this interview, Meituan senior data‑science expert Yan Peng—known as “Eureka,” a five‑star Kaggle competitor and 2017 KDD Cup champion—shares his career from Tsinghua to Meituan, the team strategies that won the traffic‑flow prediction challenge, and his advice to focus on strong mathematics, stay current with research, and study resources such as “The Elements of Statistical Learning” and NTU’s introductory machine‑learning course.

AIKDD Cupcompetition

0 likes · 7 min read

Interview with Meituan’s Data Science Expert Yan Peng on KDD Cup Success and Machine Learning Insights