让推荐更靠谱：地理近邻信息被融入语义编码，提升本地服务匹配精准度。
Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation
- 用地理中心坐标和旋转编码建模空间关系，融合语义与位置信息
- 地理聚类距离降低45.60%，Hit@50提升1.87%（工业数据集）
- 适合需要精准地理位置匹配的本地生活类推荐场景
生成式推荐系统在本地服务平台中日益普及，但仅靠语义相关性不足，还需满足严格的地理可达性。核心挑战在于语义标识符（SID）的分词方式，直接影响推荐效果。现有语义码本忽略地理约束，常导致语义相关却无法到达的推荐结果。为此，我们提出 Pro-GEO——一种感知地理邻近性的地理码本。Pro-GEO构建地理中心局部坐标系以捕捉簇内空间关系，并引入地理旋转位置编码机制，将地理邻近性建模为高维嵌入中的正交旋转变换。该设计使语义与空间信号得到均衡联合建模，而非将地理信息弱化为辅助特征。在大规模工业数据集上的实验表明，Pro-GEO显著优于当前最优方法：平均地理聚类距离降低45.60%，Hit@50提升1.87%，充分验证其在真实本地服务推荐中的有效性。
原文摘要 · Abstract (English)
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.
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