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1. Adversarial Human Trajectory Learning
for Trip Recommendation
Abstract
The problem of trip recommendation has been extensively studied in recent
years, by both researchers and practitioners. However, one of its key
aspects—understanding human mobility
the proposed methods for trip modeling rely
associated with historical points
tourists while attempting to also intertwine personal preferences
contextual topics, geospatial, and temporal aspects. However, the im
transitional preferences and semantic sequential relationships among various
POIs, along with the constraints implied by the starting point and destination
of a particular trip, have not been fully exploited. Inspired by the recent
advances in generative neural networks, in this work we propose DeepTrip
an end-to-end method for better understanding of the underlying human
mobility and improved modeling of the POIs’ transitional distribution in human
moving patterns. DeepTrip consists of: a trip encod
contextual route into a latent variable with a recurrent neural network (RNN);
Adversarial Human Trajectory Learning
for Trip Recommendation
The problem of trip recommendation has been extensively studied in recent
years, by both researchers and practitioners. However, one of its key
understanding human mobility—remains under-explored. Many of
the proposed methods for trip modeling rely on empirical analysis of attributes
associated with historical points-of-interest (POIs) and routes generated by
tourists while attempting to also intertwine personal preferences
contextual topics, geospatial, and temporal aspects. However, the im
transitional preferences and semantic sequential relationships among various
POIs, along with the constraints implied by the starting point and destination
of a particular trip, have not been fully exploited. Inspired by the recent
ative neural networks, in this work we propose DeepTrip
end method for better understanding of the underlying human
mobility and improved modeling of the POIs’ transitional distribution in human
moving patterns. DeepTrip consists of: a trip encoder (TE) to embed the
contextual route into a latent variable with a recurrent neural network (RNN);
Adversarial Human Trajectory Learning
The problem of trip recommendation has been extensively studied in recent
years, by both researchers and practitioners. However, one of its key
explored. Many of
on empirical analysis of attributes
interest (POIs) and routes generated by
tourists while attempting to also intertwine personal preferences—such as
contextual topics, geospatial, and temporal aspects. However, the implicit
transitional preferences and semantic sequential relationships among various
POIs, along with the constraints implied by the starting point and destination
of a particular trip, have not been fully exploited. Inspired by the recent
ative neural networks, in this work we propose DeepTrip—
end method for better understanding of the underlying human
mobility and improved modeling of the POIs’ transitional distribution in human
er (TE) to embed the
contextual route into a latent variable with a recurrent neural network (RNN);

2. and a trip decoder to reconstruct this route conditioned on an optimized latent
space. Simultaneously, we define an Adversarial Net composed of a
generator and critic, which generates a representation for a given query and
uses a critic to distinguish the trip representation generated from TE and
query representation obtained from Adversarial Net. DeepTrip enables
regularizing the latent space and generalizing users’ complex check-in
preferences. We demonstrate, both theoretically and empirically, the
effectiveness and efficiency of the proposed model, and the experimental
evaluations show that DeepTrip outperforms the state-of-the-art baselines on
various evaluation metrics