Route Recommendation (RR) is a core task in route planning within online navigation applications, aiming to recommend the optimal route among candidate routes to users. Industrially, RR adopts the two-stage recall-and-rank framework instead of traditional route planning algorithms primarily for computational efficiency. However, RR fundamentally differs from traditional recommendation systems that follow this paradigm. First, a primary challenge is that route items cannot be assigned unique identifiers. Additionally, RR fundamentally differs from traditional recommendation systems in its approach to feature interaction. These differences render conventional recommendation approaches inadequate for route recommendation scenarios, necessitating specialized methods that can effectively handle route-specific challenges. To address these challenges, we propose a novel method called Comprehensive Comparison Network (CCN) for route recommendation. CCN constructs comparative features by comparing non-overlapping segments between route pairs, enabling difference learning without the infinite scalability issues of ID embeddings. Furthermore, CCN employs a specially designed Comprehensive Comparison Block (CCB) that differs from previous item attention methods to achieve effective cross-interaction between routes using comparison-level features. Moreover, we develop an interpretable Pair Scoring Network (PSN) for route recommendation and introduce a more comprehensive route recommendation dataset to advance research in this field. Experimental results demonstrate the effectiveness of our method, and CCN has been successfully deployed in AMAP for over a year, demonstrating its value in route recommendation.

翻译：路线推荐（RR）是在线导航应用路线规划中的核心任务，旨在从候选路线中向用户推荐最优路线。在工业实践中，RR采用两阶段召回-排序框架而非传统路线规划算法，主要是出于计算效率的考虑。然而，RR与遵循此范式的传统推荐系统存在根本性差异。首先，一个主要挑战在于路线项目无法被分配唯一标识符。此外，RR在特征交互方法上也与传统推荐系统有本质区别。这些差异使得传统推荐方法难以适用于路线推荐场景，需要能够有效处理路线特有挑战的专门方法。为应对这些挑战，我们提出了一种名为全面比较网络（CCN）的新型路线推荐方法。CCN通过比较路线对之间的非重叠路段来构建比较特征，从而在避免ID嵌入无限扩展问题的同时实现差异学习。此外，CCN采用专门设计的全面比较模块（CCB），该模块不同于以往的物品注意力方法，能够利用比较级特征实现路线间的有效交叉交互。我们还开发了可解释的配对评分网络（PSN）用于路线推荐，并引入了更全面的路线推荐数据集以推动该领域研究。实验结果证明了我们方法的有效性，且CCN已在AMAP中成功部署超过一年，展现了其在路线推荐中的实用价值。