PFS: A Personalized Flight Recommendation Service via Cross Domain Triadic Factorization
When a passenger is going to book an airline flight, after he inputs his query conditions, he has to search from a long flight list to find the one that best matchs his preferences. Therefore, a friendly personalized flight recommender system is a necessary tool for online travel service companies. However, the majority of current approaches do not work well since in most cases, there is no enough historical order information on the route the customers may plan to take. In this paper, we propose an approach for building a Personalize Flight recommendation Service (PFS) In PFS, the attributes' information of flight orders is organized into several domains, and a tensor factorization method is used to recommend flights to users. Specifically, PFS applies a history based weighting strategy to better model users' biases. PFS is also implemented in a distributed and parallel way to increase its execution speed when dealing with a huge amount of flight data. Extensive experiments on both a real flight order dataset and a synthetic large scale dataset show the superiority of our model against other comparative ones.
2018 IEEE International Conference on Web Services (ICWS)