Utilization of Spatio-Temporal and Social Information for POI Group Recommendation
Bibliographic record
Abstract
POI group recommendation offers a list of locations to a group of users based on their visiting preferences, which is crucial for Location-Based Social Networks(LBSNs) to improve user experience quality and group satisfaction. Current studies either regard the POI as a general item for group recommendation, or do not make full use of the geospatial information of POIs and users’ social friends’ information to generate the group visiting preference representations. In this paper, a neural network-based POI group recommendation method named STSPGR is proposed. STSPGR first learns the user embedding vector that fuses temporal, spatial, categorical, and social information to represent each user’s visiting preference. Then it utilizes the attention network to dynamically learn the impact degrees of each member in the group decision-making process to aggregate the group visiting preference embedding. Finally, the POI recommender decodes the group’s embedding to the preference scores over all POIs to make the recommendation. Experiments are conducted on three real-world datasets, which show that the proposed STSPGR has better recommendation accuracy than other POI group recommendation methods.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".