Recommendations for Crowdsourcing Services Based on Mobile Scenarios and User Trajectory Awareness

With the rapid development of the mobile internet and the rapid popularization of smart terminal devices, types and content of services are changing with each passing day, these bring serious mobile information overload problems for mobile users. How to provide better service recommendations for users is an urgent problem to be solved. A crowdsourcing service recommendation strategy for mobile scenarios and user trajectory awareness is proposed. First, the location coordinates in the historical log are clustered into regions by clustering algorithms, and then the user's trajectory patterns are mined in different mobile scenarios to extract mobile rules. Furthermore, the mobile rules are extracted and the scenario to which each rule belongs is judged. When performing crowdsourcing service recommendation, the location trajectory and mobile scenario information are perceived in real time, they are used to predict the location area where the user will soon arrive, thereby the crowdsourcing service in the area is pushed to the user.