Research map / Probabilistic and scientific modeling / Variational and generative sampling
Markov-modulated Marked Poisson Processes for Check-in Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add user preference vector to basic MJP (ablation) | MJP · NYC check-in prediction, gap 0 | AUC: 0.927 -> 0.947 | Helped |
| add user preference vector to basic MJP (ablation) | MJP · NYC check-in prediction, gap 1 day | AUC: 0.870 -> 0.876 | Helped |
| add user preference vector to basic MJP (ablation) | MJP · NYC check-in prediction, gap .1 year | AUC: 0.713 -> 0.822 | Helped |
| model sequential MJP instead of LDA bag-of-locations | preference-MJP · NYC check-in prediction, gap 0 | AUC: 0.794 -> 0.947 | Helped |
| LDA vs history-all heuristic | LDA · NYC check-in prediction, gap 0 | AUC: 0.838 -> 0.794 | Hurt |
| use last 5 check-ins rather than last 1 or all history | history heuristic · NYC check-in prediction, gap .1 year | AUC: 0.710 -> 0.792 | Helped |
| increase prediction gap | MJP · NYC check-in prediction | AUC: 0.927 -> 0.713 | Hurt |
| increase prediction gap | preference-MJP · NYC check-in prediction | AUC: 0.947 -> 0.822 | Hurt |
| preference-MJP vs LDA for predicting check-in probability | preference-MJP · NYC check-in prediction probability, gap 0 | absolute error: 0.252 -> 0.190 | Helped |
| preference-MJP vs LDA on Florida data | preference-MJP · Orlando prediction, gap .1 year | AUC: 0.756 -> 0.801 | Helped |
| preference-MJP vs best baseline for anomaly detection | preference-MJP · Anomaly detection | AUC: 0.713 -> 0.864 | Helped |
| grid size of histogram baseline | grid baselines · Anomaly detection | AUC | No clear effect |
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