Location Based Services Recommendation with Budget Constraints

The paper reports the approach of our team “711” to recommend Brick-and-Mortar Store with Budget Constraints. By extensive studies, we find that the history data is very effective in this location based service recommendation and we exploit the history data in our recommendation from both the users’ aspect and the merchants’ aspect. Moreover, we extract predicative features from the train set and implement some existing methods, e.g., Xgboost. Our final ensemble method obtains a F1 score of 0.452 and reaches the third place in the SocInf’16 competition.