Disclosed are a travel package recommendation method based on a multi-view attention mechanism. A unified representation of a travel package is learned by means of deep learning technology, and a preference representation of a user is learned according to long-term and short-term click stream data of an online travel user to generate a recommendation. The method includes travel package encoding and user preference encoding. In a travel package encoding module, important words and views are selected from attributes of the travel package by using attention networks at a word level and a view level, so as to learn a unified travel package representation; and in a user preference encoding module, the dynamic evolution of a user preference is learned by means of a recurrent neural network (RNN) with an attention mechanism at a travel package level. Further provided is a gate structure fusion method, which is used for integrating long-term and short-term preferences of a user in order to learn a representation of the user. By means of the travel package recommendation method based on a multi-view attention mechanism in the present invention, useful information in a negative sample can be effectively mined, and the computing costs of model training can be reduced.
TRAVEL PACKAGE RECOMMENDATION METHOD BASED ON MULTI-VIEW ATTENTION MECHANISM