Adversarial Human Trajectory Learning for Trip Recommendation.

Area of Science:

• Artificial Intelligence
• Human Mobility Analysis
• Recommender Systems

Background:

• Trip recommendation systems often overlook human mobility patterns and sequential point-of-interest (POI) preferences.
• Existing models struggle to fully exploit transitional preferences and semantic relationships between POIs within a trip's constraints.

Purpose of the Study:

• To propose DeepTrip, an end-to-end method for enhanced understanding of human mobility and improved POI transitional distribution modeling.
• To leverage generative neural networks for more accurate trip modeling and recommendation.

Main Methods:

• Developed DeepTrip, an integrated system featuring a trip encoder (TE) using recurrent neural networks (RNN) and a trip decoder.
• Implemented an Adversarial Network with a generator and critic to regularize the latent space and distinguish trip representations.

Main Results:

• DeepTrip effectively models the latent variables of contextual routes and reconstructs trip sequences.
• The adversarial component helps generalize complex user check-in preferences and regularizes the latent space.
• Experimental evaluations demonstrate DeepTrip's superiority over state-of-the-art baselines across various metrics.

Conclusions:

• DeepTrip offers a novel approach to understanding human mobility for trip recommendation.
• The model's ability to capture sequential POI relationships and user preferences leads to improved recommendation accuracy and efficiency.