Research on Hybrid Recommendation Algorithm for Integrating Consumption Habits in LSBN

Abstract

With the rapid development of the web2.0 era and the popularity of location-based services (LBS), the development of technology and the increase in the number of users have led to location-based socialization such as Foursquare, Gowalla, Flickr, public comment, StreetSide, etc. Location-based Social Network (LBSN), LBSN-based services provide researchers with a number of meaningful research topics, such as travel route recommendations, location-based friend recommendations, trusted location recommendations, and location-based mobile advertising recommendations. In this paper, our idea is to explore the impact of user spending habits on interest point recommendations. Based on the LBSN's sign-in behavior, there is a relatively obvious distribution of consumer behavior. Therefore, the user's spending habits are obtained through the user's sign-in behavior, and the consumption habits are included in the recommendation model to improve the recommendation effect. Finally, on the real data set of Foursquare. The verification results show that our hybrid recommendation model can improve the recommendation effect.