Article citationsMore>>

Ying, J.J.C., Lu, E.H.C., Kuo, W.N. and Tseng, V.S. (2012) Urban Point-of-Interest Recommendation by Mining User Check-In Behaviors. Proceedings of the ACM SIGKDD International Workshop on Urban Computing, Beijing, 12-16 August 2012, 63-70. http://dx.doi.org/10.1145/2346496.2346507

has been cited by the following article:

• 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.