mlsanityData to 7 Oct 2026

AskNearby: An LLM-Based Application for Neighborhood Information Retrieval and Personalized Cognitive-Map Recommendations

arXiv 2512.02502 ↗First posted 2 Dec 2025cs.IR cs.AIcited by 0

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Abstract (arXiv)

The "15-minute city" envisions neighborhoods where residents can meet daily needs via a short walk or bike ride. Realizing this vision requires not only physical proximity but also efficient and reliable access to information about nearby places, services, and events. Existing location-based systems, however, focus mainly on city-level tasks and neglect the spatial, temporal, and cognitive factors that shape localized decision-making. We conceptualize this gap as the Local Life Information Accessibility (LLIA) problem and introduce AskNearby, an AI-driven community application that unifies retrieval and recommendation within the 15-minute life circle. AskNearby integrates (i) a three-layer Retrieval-Augmented Generation (RAG) pipeline that synergizes graph-based, semantic-vector, and geographic retrieval with (ii) a cognitive-map model that encodes each user's neighborhood familiarity and preferences. Experiments on real-world community datasets demonstrate that AskNearby significantly outperforms LLM-based and map-based baselines in retrieval accuracy and recommendation quality, achieving robust performance in spatiotemporal grounding and cognitive-aware ranking. Real-world deployments further validate its effectiveness. By addressing the LLIA challenge, AskNearby empowers residents to more effectively discover local resources, plan daily activities, and engage in community life.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 2 Dec 2025.

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11 of 42 references.

References · 11

  1. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  2. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  3. 2024Large Language Models for Next Point-of-Interest Recommendationcited by 40
  4. 2024Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learningcited by 15
  5. 2025TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planningcited by 12
  6. 2021Spatial Object Recommendation with Hints: When Spatial Granularity Matterscited by 11
  7. 2018Personalized Context-Aware Point of Interest Recommendationcited by 4
  8. 2019A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendationcited by 4
  9. 2024Exploring Spatial Representations in the Historical Lake District Texts with LLM-based Relation Extractioncited by 2
  10. 2024Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Eventscited by 1
  11. 2025Neighborhood Disparities in Smart City Service Adoptioncited by 1

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