Towards Full Candidate Interaction: A Comprehensive Comparison Network for Better Route Recommendation

TL;DR

This paper introduces a novel comprehensive comparison network (CCN) for route recommendation that effectively handles route-specific challenges, improves feature interaction, and has been successfully deployed in real-world navigation systems.

Contribution

The paper proposes a new CCN model with comparison-based features and a specialized comparison block, addressing unique route recommendation challenges and outperforming traditional methods.

Findings

CCN effectively models route-specific feature interactions.

CCN outperforms baseline methods in experiments.

Deployed in AMAP for over a year with positive results.

Abstract

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.