Abstract
With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.
Highlights
The rapid growth of cities has led to an increase in the number of points of interest (POIs), e.g., restaurants, theaters, stores, hotels, to enrich people’s life and entertainment, providing us with more choices of life experience than ever before
With the rapid pervasiveness of mobile devices embedded with wireless communication and location sensors, they form into a internet of everything
We focused on Point-location-based location-based social network
Summary
The rapid growth of cities has led to an increase in the number of points of interest (POIs), e.g., restaurants, theaters, stores, hotels, to enrich people’s life and entertainment, providing us with more choices of life experience than ever before. Sensors 2019, 19, 992 the most popular Internet applications and attracted millions of users as they help solve the problem of finding places in a specific physical geographical area for users Through these applications, across the world, individuals share their footprints, opinions, experiences and contribute assorted forms of location-specific multimedia contents by declaring their presence by an action known as a check-in which is very helpful for individuals wishing to find a new restaurants, events, bars, and etcetera. As can be observed from the figure in addition to the social networking structure between users, they can share their footprints, opinions, experiences and contribute assorted forms of location-specific multimedia contents in a LBSNs through an action called “check-in” at a point of interest(POI) using their mobile devices
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