Probabilistic Category-based Location Recommendation Utilizing Temporal Influence and Geographical Influence
Bibliographic record
Abstract
Location recommendation provides unvisited locations to the users for the rapidly growing location-based social networks. The service is based on the users' visiting histories and location related information such as location categories. In this paper, we propose a location recommendation algorithm called sPCLR that recommends locations to the users at a given time of the day by utilizing category information. The algorithm considers both temporal and spatial components. The temporal component utilizes the temporal influence of similar users' check-in behaviors by representing a user's periodic check-in behavior at different location categories as temporal curves. The similarity between users' periodic check-in behavior is calculated based on the difference between temporal curves. The spatial component utilizes the geographical influence of locations and filters out those locations that are not of interest to the user. The performance of sPCLR is compared with three existing location recommendation algorithms on a real-world dataset. Experimental results show that the sPCLR algorithm performs better than all other three algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".