Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation
- URL: http://arxiv.org/abs/2604.23156v1
- Date: Sat, 25 Apr 2026 06:05:41 GMT
- Title: Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation
- Abstract summary: Pro-GEO is a Proximity-aware GEO-codebook.<n>Pro-GEO significantly outperforms state-of-the-art methods.<n>In particular, Pro-GEO reduces the average geographic clustering distance by 45.60%.
- Score: 19.15318282536391
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Generative recommendation systems are increasingly adopted in local service platforms, where semantic relevance alone is insufficient without strict geographic feasibility. A key technical challenge lies in semantic ID (SID) tokenization, which directly impacts the recommendation performance. However, existing semantic codebooks neglect geographic constraints, often resulting in recommendations that are semantically relevant yet geographically unreachable. To address this limitation, we propose Pro-GEO, a Proximity-aware GEO-codebook. Pro-GEO establishes a geo-centroid local coordinate system to capture intra-cluster spatial relationships and a geo-rotary position encoding mechanism that models geographic proximity as orthogonal rotational transformations in the high-dimensional embedding. This design enables semantic and spatial signals to be jointly modeled in a balanced manner, without reducing geographic information to a weak auxiliary feature. Extensive experiments conducted on a large-scale industrial dataset reveal that Pro-GEO significantly outperforms state-of-the-art methods. In particular, Pro-GEO reduces the average geographic clustering distance by 45.60% and achieves a 1.87% improvement in Hit@50, highlighting its effectiveness for real-world local service recommendation.
Related papers
- ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs [8.089023511080248]
This paper proposes ReaGeo, an end-to-end geocoding framework based on large language models.<n>The method converts geographic coordinates into geohash sequences, reformulating the coordinate prediction task as a text generation problem.<n>Reinforcement learning with a distance-deviation-based reward is applied to optimize the generation accuracy.
arXiv Detail & Related papers (2026-04-23T07:18:21Z) - Spatially-Weighted CLIP for Street-View Geo-localization [5.8048364242505945]
This paper proposes a novel framework for street-view geo-localization that explicitly incorporates spatial autocorrelation into vision-language contrastive learning.<n>Experiments on a multi-city dataset demonstrate that SW-CLIP significantly improves geo-localization accuracy, reduces long-tail errors, and enhances spatial coherence compared to standard CLIP.
arXiv Detail & Related papers (2026-04-06T02:10:36Z) - Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning [72.13218601075958]
We introduce textbfWanderBench, the first open access global geolocation benchmark for actionable geolocation reasoning in embodied scenarios.<n>We propose textbfGeoAoT (Action of Thought), a underlineGeolocation framework with underlineAction of underlineThough, which couples reasoning with embodied actions.<n>Experiments on 19 large multimodal models show that GeoAoT achieves superior fine-grained localization and stronger generalization in dynamic environments.
arXiv Detail & Related papers (2026-03-11T06:24:10Z) - GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
arXiv Detail & Related papers (2026-02-13T04:48:05Z) - GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving [55.14836667214487]
GeoFocus is a novel framework comprising two core modules.<n>GeoFocus achieves a 4.7% accuracy improvement over leading specialized models.<n>It demonstrates superior robustness in MATHVERSE under diverse visual conditions.
arXiv Detail & Related papers (2026-02-09T11:15:01Z) - Performance and Generalizability Impacts of Incorporating Location Encoders into Deep Learning for Dynamic PM2.5 Estimation [0.39762912548964846]
We study the effect of geolocation into deep learning for a dynamic and spatially heterogeneous application.<n>We estimate daily surface-level PM2.5 across the contiguous United States using satellite and ground-based observations.<n>Results show that raw coordinates can improve performance within regions by supporting spatial generalization, but can reduce generalizability across regions.
arXiv Detail & Related papers (2025-05-24T02:00:34Z) - Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
arXiv Detail & Related papers (2025-02-19T14:21:25Z) - Memory efficient location recommendation through proximity-aware
representation [8.505840656442217]
We introduce a Proximity-aware based region representation for Sequential Recommendation (PASR)
We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization.
We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods.
arXiv Detail & Related papers (2023-10-10T09:53:07Z) - Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese
Geographic Re-Ranking [61.60169764507917]
Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates.
We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
arXiv Detail & Related papers (2023-09-04T13:44:50Z) - GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
We study the problem of geographic robustness and make three main contributions.
First, we introduce a large-scale dataset GeoNet for geographic adaptation.
Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context.
Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
arXiv Detail & Related papers (2023-03-27T17:59:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.