Name
Abstract | ||
|---|---|---|
With the growing popularity of location-based social networks (LBSNs), time-specific POI recommendation has become important in recent years, which provides more accurate recommendation services for users in specific spatio-temporal contexts. In this paper, we propose a spatio-temporal distance metric embedding model (ST-DME) for time-specific recommendation, which exploits both temporal and geo-sequential property of a check-in to effectively model users' time-specific preferences. Specifically, we divide timestamps of user' check-ins into different time slots and adopt Euclidean distance rather than inner product of latent vectors to measure users' preferences for POIs in a given time slot. We also apply a transition coefficient based on users' most recent check-ins to incorporate geo-sequential influence in users' check-in behaviors. A weighted pairwise loss with a hard sampling strategy is adopted to optimize latent vectors of users, POIs, and time slots in a metric space. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method and results show that ST-DME outperforms state-of-the-art algorithms for time-specific POI recommendation on two public LBSNs data sets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2869994 | IEEE ACCESS |
Keywords | Field | DocType |
Time-specific POI recommendation,location-based social networks,distance metric embedding | Recommender system,Data mining,Pairwise comparison,Embedding,Computer science,Euclidean distance,Metric (mathematics),Context model,Timestamp,Metric space,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruifeng Ding | 1 | 1 | 0.69 |
| Zhenzhong Chen | 2 | 1244 | 101.41 |
| Xiaolei Li | 3 | 17 | 5.16 |