Skyyyy0920/MTNet
Code implementation for our paper "Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation" (AAAI-2024)
This project helps location-based service providers and marketers predict where people will go next, like predicting a customer's next coffee shop visit or a tourist's next attraction. By analyzing historical location data from apps and social networks, it generates recommendations for specific points of interest based on the time of day. Anyone managing a location-aware app, urban planner, or business owner looking to optimize location-based marketing can use this.
No commits in the last 6 months.
Use this if you need to recommend the next best location to a user, considering not just their past movements but also their typical preferences during different times of the day.
Not ideal if your recommendation needs are not location-based, or if you don't have historical check-in data with timestamps.
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22
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2
Language
Python
License
MIT
Category
Last pushed
Jun 11, 2025
Commits (30d)
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