Spatial-Temporal Deep Intention Destination Networks for Online Travel
  Planning
  Nowadays, artificial neural networks are widely used for users' online travel
planning. Personalized travel planning has many real applications and is
affected by various factors, such as transportation type, intention destination
estimation, budget limit and crowdness prediction. Among those factors, users'
intention destination prediction is an essential task in online travel
platforms. The reason is that, the user may be interested in the travel plan
only when the plan matches his real intention destination. Therefore, in this
paper, we focus on predicting users' intention destinations in online travel
platforms. In detail, we act as online travel platforms (such as Fliggy and
Airbnb) to recommend travel plans for users, and the plan consists of various
vacation items including hotel package, scenic packages and so on. Predicting
the actual intention destination in travel planning is challenging. Firstly,
users' intention destination is highly related to their travel status (e.g.,
planning for a trip or finishing a trip). Secondly, users' actions (e.g.
clicking, searching) over different product types (e.g. train tickets, visa
application) have different indications in destination prediction. Thirdly,
users may mostly visit the travel platforms just before public holidays, and
thus user behaviors in online travel platforms are more sparse, low-frequency
and long-period. Therefore, we propose a Deep Multi-Sequences fused neural
Networks (DMSN) to predict intention destinations from fused multi-behavior
sequences. Real datasets are used to evaluate the performance of our proposed
DMSN models. Experimental results indicate that the proposed DMSN models can
achieve high intention destination prediction accuracy.
