Intelligent location-aware recommender system for urban micro-connections using general graph matching

Abstract

The rise of conurbations exacerbates "urban loneliness," yet current connection platforms suffer from appearance bias, high cancellation rates, and inadequate safety constraints. To address these issues, our study proposes.SLOT, a real-time 1-on-1 social interaction system. It integrates anonymous recognition and a Location-Aware Recommender System to securely anchor meeting points in public spaces, optimizing bilateral converging distances. The core contribution is the multi-objective matching engine, solving the allocation problem on a generalized graph using Edmonds' Blossom algorithm. Combining time-window batching, elastic pruning, and symmetric credit scoring, our system empirically achieves a balanced multi-objective trade-off among liquidity, converging distance, and fairness. Compared to Weighted-sum Greedy at 3.8 and Distance-based Greedy at 13.0,.SLOT  compresses credit deviation to 0.9 with a minor 1.8% trade-off in matching rate. Simulations on a synthetic dataset of 1,000 agents within a university campus micro-zone demonstrate that by spatially decomposing requests into micro-Anchor Stations, the system achieves sub-second latency, mitigating the O(V³) computational bottleneck of direct global batching.

Conflict of Interest

The author declares that there are no financial or personal relationships that could be perceived as inappropriately biasing the work reported in this manuscript.