# Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

arXiv: https://arxiv.org/abs/1907.08489 · HTML: https://mlsanity.com/paper/1907.08489/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 21 of 21)

- [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04475/) (2022)
- [Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics](https://mlsanity.com/paper/2211.09510/) (2022)
- [Continuous Trajectory Generation Based on Two-Stage GAN](https://mlsanity.com/paper/2301.07103/) (2023)
- [INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging](https://mlsanity.com/paper/2302.02738/) (2023)
- [BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis](https://mlsanity.com/paper/2412.00953/) (2024)
- [Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning](https://mlsanity.com/paper/2502.06870/) (2025)
- [AttnMove: History Enhanced Trajectory Recovery via Attentional Network](https://mlsanity.com/paper/2101.00646/) (2021)
- [Full Bayesian Significance Testing for Neural Networks](https://mlsanity.com/paper/2401.13335/) (2024)
- [Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks](https://mlsanity.com/paper/2302.10903/) (2023)
- [Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense](https://mlsanity.com/paper/2304.06919/) (2023)
- [A Survey of Route Recommendations: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2403.00284/) (2024)
- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation](https://mlsanity.com/paper/2109.03535/) (2021)
- [R4: A Framework for Route Representation and Route Recommendation](https://mlsanity.com/paper/2110.10474/) (2021)
- [Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems](https://mlsanity.com/paper/2511.17435/) (2025)
- [The Optimized path for the public transportation of Incheon in South Korea](https://mlsanity.com/paper/2309.10006/) (2023)
- [Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding](https://mlsanity.com/paper/2511.08978/) (2025)
- [A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems](https://mlsanity.com/paper/2512.14728/) (2025)
- [From Feasibility to Desirability: Plan, Learn, Adapt (PLA) Framework for Personalized On-Device Itinerary Generation](https://mlsanity.com/paper/2607.15552/) (2026)
- [Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping](https://mlsanity.com/paper/2608.06105/) (2026)
- [Customizable and Jointly Optimized Route Planning: A Deep Architecture Enabling Differentiable Shortest-Path Search](https://mlsanity.com/paper/2609.19996/) (2026)

## Abstract (arXiv)

Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users' route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.