Name
Abstract | ||
|---|---|---|
Due to the prevalence of human activity in urban spaces, recommending ROIs (region-of-interests) to users, especially irregular ROIs, becomes an important task in location-based social networks. A fundamental problem is how to aggregate users’ preferences over POIs (point-of-interests) to infer the users’ region-level mobility patterns. The majority of existing studies ignore the users’ implicit interactions with individual POIs when addressing this issue. For example, a user check-in a region cannot provide any specific information about how the user likes this region (we call this phenomenon “ROI-level” implicitness) and which POI in this region the user is interested in (i.e., “POI-level” implicitness). Furthermore, existing studies adopt predefined strategies for region-level preference aggregation, that is, initializing the importance of different POIs with identical weights, which is insufficient to model the reality of social networks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.adhoc.2020.102383 | Ad Hoc Networks |
Keywords | DocType | Volume |
Location-based social network,Region mobility pattern,Region recommendation,Attention mechanism,Graph neural network | Journal | 113 |
ISSN | Citations | PageRank |
1570-8705 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengpeng Xu | 1 | 0 | 0.34 |
| Jun Wang | 2 | 0 | 0.34 |
| Jinmao Wei | 3 | 23 | 6.46 |