Journal
IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 9, Issue 4, Pages 633-646Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2015.2413783
Keywords
Location-based social networks; location recommendations; time-aware location recommendations; continuous temporal influence; temporal influence correlations; kernel density estimation
Funding
- Guangdong Natural Science Foundation of China [S2013010012363]
- CityU [9231131]
Ask authors/readers for more resources
In location-based social networks (LBSNs), time significantly affects users' check-in behaviors, for example, people usually visit different places at different times of weekdays and weekends, e.g., restaurants at noon on weekdays and bars at midnight on weekends. Current studies use the temporal influence to recommend locations through dividing users' check-in locations into time slots based on their check-in time and learning their preferences to locations in each time slot separately. Unfortunately, these studies generally suffer from two major limitations: (1) the loss of time information because of dividing a day into time slots and (2) the lack of temporal influence correlations due to modeling users' preferences to locations for each time slot separately. In this paper, we propose a probabilistic framework called TICRec that utilizes temporal influence correlations (TIC) of both weekdays and weekends for time-aware location recommendations. TICRec not only recommends locations to users, but it also suggests when a user should visit a recommended location. In TICRec, we estimate a time probability density of a user visiting a new location without splitting the continuous time into discrete time slots to avoid the time information loss. To leverage the TIC, TICRec considers both user-based TIC (i.e., different users' check-in behaviors to the same location at different times) and location-based TIC (i.e., the same user's check-in behaviors to different locations at different times). Finally, we conduct a comprehensive performance evaluation for TICRec using two real data sets collected from Foursquare and Gowalla. Experimental results show that TICRec achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques with temporal influence.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available