Encoder-Decoder Based Route Generation Model for Flexible Travel Recommendation

Travel route recommendation is an important part of electronic tour guides and map applications. It aims to recommend a sequence of points of interest (POIs) to users based on their interests. The variety of users' historical records and their requirements makes the problem challenging and most...

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Bibliographic Details
Published in:IEEE transactions on services computing Vol. 17; no. 3; pp. 905 - 920
Main Authors: Zhang, Jiale, Ma, Mingqian, Gao, Xiaofeng, Chen, Guihai
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
bi-directional LSTM
encoder-decoder
Encoders-Decoders
Feature extraction
grid beam search
Heuristic algorithms
Long short term memory
Machine learning
Machine learning algorithms
Optimization
POI
Search algorithms
Semantics
SVD
Travel route recommendation
User requirements
ISSN:1939-1374, 2372-0204
Online Access:Get full text
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Summary:Travel route recommendation is an important part of electronic tour guides and map applications. It aims to recommend a sequence of points of interest (POIs) to users based on their interests. The variety of users' historical records and their requirements makes the problem challenging and most existing works fail to satisfy these two aspects at the same time. In this article, we propose an encoder-decoder-based travel route recommendation framework, to help electronic tourist guide applications better recommend routes for their users. The framework makes accurate and flexible route recommendations by combining encoder-decoder structure with grid beam search. We make feature extraction and feature completion with domain knowledge and matrix factorization methods. Then, an encoder-decoder structure with a dual bi-directional LSTM encoder is proposed as a basis for route generation. Finally, we select the routes by grid beam search algorithm to give efficient recommendations. Multiple explicit requirements can be supported in our model, including unavailable POIs, mandatory POIs, restricted sequence length, and dynamic route revision. Experiments on eight real-world datasets show that our model achieves a 9.8% improvement in performance, compared with the state-of-the-art and supports more explicit requirements.
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ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2024.3376231