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Encoder-Decoder Based Route Generation Model for Flexible Travel Recommendation
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AI
DOI：10.1109/TSC.2024.3376231.png)
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摘要
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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.
Keyword:
Semantics
Optimization
Machine learning
Machine learning algorithms
Heuristic algorithms
Long short term memory
Feature extraction
Travel route recommendation
POI
bi-directional LSTM
encoder-decoder
grid beam search
SVD
期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
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