Research map / Graph learning / Knowledge and temporal graphs
An Attentional Recurrent Neural Network for Personalized Next Location Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model neighbor transition regularities with attention (ARNN) vs LSTM | LSTM · Foursquare NY | Acc@1: 0.1384 -> 0.1738 | Helped |
| ARNN vs best baseline Caser | next location recommendation · Foursquare TK | Acc@20: 13.09% | Helped |
| cumulatively add meta-paths (Geo, Sem, User) for neighbor discovery (ablation) | ARNN · NY/TK/SF | Acc@1: 14.69% | Helped |
| add LVL and LL meta-paths (ablation) | ARNN · NY/TK/SF | Acc@1 | No clear effect |
| increase time threshold from 12h to 1d/2d (ablation) | ARNN · Foursquare TK | Acc@1: 0.1867 -> 0.1321 | Hurt |
| decrease time threshold from 12h to 3h (ablation) | ARNN · SF | Acc@1: 0.1324 -> 0.0699 | Hurt |
| decrease time threshold from 12h to 6h (ablation) | ARNN · Foursquare NY | Acc@1: 0.1738 -> 0.2058 | Mixed |
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