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Network Intelligence Research Center (NIRC)

Jan 17, 2026 · Artificial Intelligence

DiffNBR: A Spatiotemporal Diffusion and Information‑Bottleneck Approach for Next‑Basket Recommendation

DiffNBR introduces a dual‑path diffusion framework combined with an information‑bottleneck mechanism to jointly model spatial co‑occurrence and temporal evolution in next‑basket recommendation, achieving state‑of‑the‑art performance and effectively disentangling repetitive and exploratory purchase patterns.

DiffNBRdiffusion modelinformation bottleneck

0 likes · 8 min read

DiffNBR: A Spatiotemporal Diffusion and Information‑Bottleneck Approach for Next‑Basket Recommendation

Amap Tech

Dec 29, 2025 · Artificial Intelligence

How G‑Plan Transforms Map Recommendations with AI Agents and Multi‑Demand Planning

This article details how Gaode's G‑Plan combines large‑model AI agents, generative ranking, and spatiotemporal counterfactual DPO to model and prioritize multiple user intents on the home page, presents the system architecture, experimental setup, online gains, and ablation results, and explains how it moves recommendation from passive to proactive planning.

AI recommendationintent planninglarge language model

0 likes · 21 min read

How G‑Plan Transforms Map Recommendations with AI Agents and Multi‑Demand Planning

Amap Tech

Nov 4, 2025 · Artificial Intelligence

Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations

This article introduces Spacetime‑GR, a large‑scale generative recommendation model that integrates hierarchical geographic POI indexing and spatiotemporal token encoding to enhance POI prediction for Amap, detailing its pre‑training pipeline, data cleaning, curriculum learning strategy, experimental results, scaling law observations, and the resulting improvements in hit rate and discovery rate.

AmapPOI recommendationcurriculum learning

0 likes · 14 min read

Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations

Ele.me Technology

Aug 17, 2023 · Artificial Intelligence

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

BASM is a bottom‑up adaptive spatiotemporal model for online food ordering that uses hierarchical embedding, semantic transformation, and adaptive bias layers to dynamically modulate parameters according to time and location, thereby capturing multiple data distributions and achieving superior offline metrics and online A/B test performance.

CTR predictionRecommendation Systemsadaptive parameters

0 likes · 18 min read

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

Ele.me Technology

Aug 16, 2023 · Artificial Intelligence

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

The paper introduces StEN, a spatiotemporal-enhanced network for CTR prediction in location-based services, combining static spatiotemporal feature activation, dynamic preference activation, and target attention, achieving state-of-the-art offline results and a 1.6% CTR lift in online tests.

Deep LearningRecommendation Systemsclick-through rate

0 likes · 19 min read

Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location‑Based Services

DataFunTalk

Dec 17, 2022 · Artificial Intelligence

Efficient Spatiotemporal Self‑Attention Transformer (Patch Shift Transformer) for Video Action Recognition

This article introduces a lightweight spatiotemporal self‑attention transformer, called Patch Shift Transformer, which achieves competitive video action recognition performance on datasets such as Kinetics‑400, Sth‑v1/v2, and Diving48 without increasing computational cost or parameters, and details its design, experiments, and speed advantages.

ECCV 2022Transformerpatch shift

0 likes · 5 min read

Efficient Spatiotemporal Self‑Attention Transformer (Patch Shift Transformer) for Video Action Recognition

Meituan Technology Team

Nov 24, 2022 · Artificial Intelligence

Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment

The article details Meituan's deployment of large-scale heterogeneous graph recall for in‑store recommendation ads, covering full‑scene graph construction, graph pruning, dynamic negative sampling, spatiotemporal sub‑graph fusion, and performance optimizations that together raise offline hit‑rate by over 5% and online revenue per search by 10‑15%.

Large-Scale TrainingMeituangraph neural networks

0 likes · 25 min read

Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment

Meituan Technology Team

Oct 14, 2021 · Artificial Intelligence

Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising

Meituan's ad team uses deep learning to handle LBS distance constraints and long‑term periodic behavior, introducing DPIN for position/context bias, an ultra‑long sequence encoder with spatiotemporal activator, dynamic candidate generation, and memory‑augmented continual learning, boosting RPM 2‑20% and enabling sub‑millisecond inference.

AdvertisingCTR predictionDeep Learning

0 likes · 29 min read

Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising