Spatio-Temporal Topic Models for Check-in Data
Why this work is in the frame
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Bibliographic record
Abstract
Twitter, together with other online social networks, such as Facebook, and Gowalla have begun to collect hundreds of millions of check-ins. Check-in data captures the spatial and temporal information of user movements and interests. To model and analyze the spatio-temporal aspect of check-in data and discover temporal topics and regions, we propose two spatio-temporal topic models: Downstream Spatio-Temporal Topic Model (DSTTM) and Upstream Spatio-Temporal Topic Model (USTTM). Both models can discover temporal topics and regions. We use continuous time to model check-in data, rather than discretized time, avoiding the loss of information through discretization. In order to capture the property that user's interests and activity space will change over time, we propose the USTTM, where users have different region and topic distributions at different times. We conduct experiments on Twitter and Gowalla data sets. In our quantitative analysis, we evaluate the effectiveness of our models by the perplexity, the accuracy of POI recommendations, and user prediction, demonstrating that our models achieve better performance than the state-of-the-art models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it