Multi-objective Trip Recommendation Based on Check-in Data and Traffic Tools

In general, users want to spend little money to visit as many attractions with different categories and high scores as possible. In order to solve this problem, this paper proposes a multi-constrained and multi-objective pruning algorithm. First, it analyzes users' historical check-in data, and proposes a rating mechanism of scenery spot based on users' check-in data-know users’ preferences and popularity of scenery spot. And this paper considers the different choices of vehicles, because the transfer between attractions will have different transfer time and costs. Finally, according to users’ needs, the MCPA-MO algorithm is used to calculate an optimal travel route for the user. The recommendation algorithm was implemented and evaluated using the real data on the Foursquare and the information on the famous attractions in Chengdu on the Horse Cell as the data set. Experimental results show that it can recommend personalized trip that meet the actual needs of users.