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Multi-view Graph Attention Network for Travel Recommendation by Lei Chen; Jie Cao; Youquan Wang; Weichao Liang; Guixiang Zhu is a Computer Science article available to read on EtoBox.

What is Multi-view Graph Attention Network for Travel Recommendation about?

As an e-commerce feature, the recommender system can enhance the consumer shopping experience and create huge benefits for businesses. The e-tourism has become one of the largest service industries with the application and popularity of recommender systems. Many studies have confirmed that the travel product recommendation is widely different from traditional recommendations. Due to the financial and time costs, travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books, movies and food). In addition, choosing the appropriate travel product will be influenced by many factors, such as departure, destination and price. To tackle this challenging problem, we propose a MV-GAN (short for Multi-View Graph Attention Network for travel recommendation) model. It enriches user and product semantics through both metapath-guided neighbors aggregation and multi-view fusion in heterogeneous travel product recommendation graph. In particular, we design node-level and path-level attention networks for learning user and product representations from every single view. To collaboratively integrate multiple types of relationships in

Who reads Multi-view Graph Attention Network for Travel Recommendation?

Author

Lei Chen; Jie Cao; Youquan Wang; Weichao Liang; Guixiang Zhu

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)