Recommendation of tourist attractions based on user preferences and attractions popularity

At present, the recommendation of information for travel is concentrating on two parts: personalized travel recommendations and classic travel recommendations. However, the interaction relationship between the user preference and the classic attractions should also be taken into consideration. Comprehensive the above consideration, this essay propose a more effective and precise algorithm named CIAP (Combination of Interest and Popularity), which based on personal interests and attractions popularity. This algorithm extracts users' interest matrix from users' GPS trajectories to build a core user model. Based on this mode, CAIP defines user similarity function and attractions popular degree function, then CIAP gets the optimal results of recommendation by determining similarity of user's attractions weight and attractions popularity weight. We evaluate the recommended effect of CIAP algorithm in different weight based on GPS data which is collected in the Geolife project (Microsoft Research Asia), that show our method has a better comprehensive performance.