Search Suggestion

Architect

May 19, 2022 · Artificial Intelligence

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

This article details Amap's practical experience with Learning to Rank for search suggestions, covering application scenarios, data pipeline construction, feature engineering, model training, loss‑function adjustments, and the resulting performance improvements, while also discussing challenges such as sparse features and click bias.

AMapSearch Suggestionfeature engineering

0 likes · 9 min read

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

DataFunTalk

Jun 27, 2020 · Artificial Intelligence

Applying Learning to Rank for Search Suggestions at Gaode Maps

This article details how Gaode Maps leveraged machine‑learning‑based Learning to Rank to rebuild its search‑suggestion ranking pipeline, addressing challenges in sample construction, feature sparsity, and model optimization, and achieving significant improvements in relevance metrics and user experience.

Gaode MapsRankingSearch Suggestion

0 likes · 9 min read

Applying Learning to Rank for Search Suggestions at Gaode Maps

Amap Tech

Jun 5, 2019 · Artificial Intelligence

Applying Learning to Rank for Search Suggestion Optimization at Gaode Maps

Gaode Maps applied Learning to Rank to optimize search suggestions, moving from rule-based to gradient boosted rank model, addressing sample construction and feature sparsity via session-based labeling and loss adjustment, achieving a seven‑point MRR gain and higher coverage, and paving the way for personalization and deep learning.

Gaode MapsSearch Suggestionlearning to rank

0 likes · 11 min read