Relevance Optimization in Neighborhood-Scale Search Using Proximity and Freshness Signals: A Hybrid AI Retrieval Architecture For Hyperlocal Community Platforms

Citation: Venkata Karunakara Reddy Revunuru, "Relevance Optimization in Neighborhood-Scale Search Using Proximity and Freshness Signals: A Hybrid AI Retrieval Architecture For Hyperlocal Community Platforms", Universal Library of Engineering Technology, Volume 03, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article examines the design of geo-aware information-retrieval pipelines for hyperlocal community platforms, where ranking quality depends on small-radius distance, rapid content turnover, and sparse, noisy texts. The relevance problem is framed as a hybrid retrieval task that unifies lexical matching and embedding-based similarity while incorporating proximity and freshness as first-class ranking signals. The work targets an architecture that reduces engineering friction by consolidating sparse and dense retrieval within a single operational search stack and by utilizing fusion methods that remain stable under shifting query intent. The study outlines scoring functions for distance and time decay, candidate-generation strategies under geographic constraints, and ranking fusion options to balance precision and recall. Source materials cover hybrid rank fusion, integration of Lucene-based dense retrieval, geo-tagged vector querying, and spatial–keyword indexing. The article provides practical guidance for platform teams building neighborhood-scale discovery systems that rank results based on user intent, local time, and real-time availability, rather than proximity and freshness alone. Safety and moderation are treated as low-latency eligibility gates integrated into both sparse and dense retrieval paths. A concrete WingBud-style example illustrates intent-driven matching and contextual ranking for short-duration neighborhood interactions.

Keywords: Hybrid Information Retrieval, Geo-Aware Search, Neighborhood-Scale Ranking, Intent-Aware Ranking, Availability-Aware Scoring, Real-Time Safety And Moderation.

https://doi.org/10.70315/uloap.ulete.2026.0302017