TITLE:
User Preferences-Based and Time-Sensitive Location Recommendation Using Check-In Data
AUTHORS:
Shaowu Zhang, Kejiang Ren
KEYWORDS:
LBS, Location Recommendation, Text Mining
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.9,
September
7,
2015
ABSTRACT: Location-based social networks have
attracted increasing users in recent years. Human movements and mobility patterns
have a high degree of freedom and provide us with a lot of trajectory to
understand the activity of users. In this paper, we presenta user
preferences and time sensitive recommender systems that offer an appropriate
venue for a user when he appears in a special time at a particular location.
The system considering the factors are: 1) the popularity of a location; 2) the
preferences of a user; 3) social influence of the friends of the user and the
friends who are check-in at the same location with the user; and 4) the time
feature of the location and the user visiting. We evaluate our system with a
large-scale real dataset from a location-based social network of Gowalla. The
results confirm that our method provides more accurate location recommendations
compared to the baseline.