An Algorithm for LBS-based Schedule Recommendation with Time Constraint

Recently, Point of Interest Recommendation is widely used in LBS navigation systems. It makes use of the real-time GPS locations of users as well as their preferences to recommend POIs that mostly match these preferences and the paths leading to the POIs. Previous studies are focused on the following two issues: (1) Similarity measurement between POIs and the user preferences, and (2) Optimum path selection from the user location to the recommended POIs. However in most scenarios, users need not only some isolated POIs, but a combination of several POIs that covers the user preferences as well as the paths connecting them. Essentially, it is the schedule planning problem. Schedule planning is usually with strict time limits, and equivalent to Generalized Traveling Sales Man Problem (GTSP) which was proved a NP-hard problem. This imposes great challenge to its solution. In this paper, we formalize the problem of Schedule Planning with strict Time Constraint (SPwTC). Especially, we wrap the static paths between POIs into route activities, thus a globally unified model of user activity can be defined. Based on Genetic Algorithm, we propose the schedule recommendation algorithm to generate candidate route plans. Subsequently, we propose the recommendation function for sorting the recommended schedule plans so as to make the recommended result more in line with user expectation. At the end of this paper, we verify the efficiency of the algorithm as well as its rationality of the recommended result with real road network data.