Bibliographic record
Abstract
Point of Interest (POI) recommender systems help provide their users with a location or place that they might be interested in visiting.When combined with Location-Based Social Networks (LBSNs), POI recommender systems can be restructured to recommend POI for groups of users and not just individuals.The research focused on Group Recommender Systems (GRSs) and specifically, POI GRSs are scarce when compared to recommender systems for individuals.There are two main techniques that are used for POI GRSs, the Group Profiling methods and the Users Score Aggregation methods.Both methods have their drawbacks, as the Group Profiling methods do not recommend well for new groups and the Users Score Aggregation methods generally do not perform as well as the Group Profiling methods for established groups.In this paper, we propose new result aggregation methods that use both the Group Profiling method and the Users Score Aggregation method's results to provide the best POI group recommendations without the aforementioned drawbacks.Using the check-ins of a group's subgroups and supergroups, we formulated connection and typicality metrics to establish an influencing factor for each user in the decision-making process of the group.The proposed result aggregation methods are tested against various POI GRSs.The tests showed that the result aggregation methods did outperform both the Group Profiling methods and the Users Score Aggregation methods, as well as showing that subgroups and supergroups check-ins are effective for establishing user influence factors.i B.63 GTT results for new groups using POP on Mexico City's dataset . .C.1 LM results using SGD on Istanbul's dataset . . . . . . . . . . . . . .C.2 AVM results using SGD on Istanbul's dataset . . . . . . . . . . . . .C.3 AVG results using SGD on Istanbul's dataset . . . . . . . . . . . . . .C.4 UUI results using SGD on Istanbul's dataset . . . . . . . . . . . . . .C.5 UUT results using SGD on Istanbul's dataset . . . . . . . . . . . . .C.6 LM results using IALS on Istanbul's dataset . . . . . . . . . . . . . .C.7 AVM results using IALS on Istanbul's dataset . . . . . . . . . . . . .C.8 AVG results using IALS on Istanbul's dataset . . . . . . . . . . . . .C.9 UUI results using IALS on Istanbul's dataset . . . . . . . . . . . . . .C.10 UUT results using IALS on Istanbul's dataset . . . . . . . . . . . . .C.11 LM results using POP on Istanbul's dataset . . . . . . . . . . . . . .C.12 AVM results using POP on Istanbul's dataset . . . . . . . . . . . . .C.13 AVG results using POP on Istanbul's dataset . . . . . . . . . . . . .C.14 UUI results using POP on Istanbul's dataset . . . . . . . . . . . . . .x C.15 UUT results using POP on Istanbul's dataset . . . . . . . . . . . . .C.16 LM results using SGD on Izmir's dataset . . . . . . . . . . . . . . . .C.17 AVM results using SGD on Izmir's dataset . . . . . . . . . . . . . . .C.18 AVG results using SGD on Izmir's dataset . . . . . . . . . . . . . . .C.19 UUI results using SGD on Izmir's dataset . . . . . . . . . . . . . . .C.20 UUT results using SGD on Izmir's dataset . . . . . . . . . . . . . . .C.21 LM results using IALS on Izmir's dataset . . . . . . . . . . . . . . . .C.22 AVM results using IALS on Izmir's dataset . . . . . . . . . . . . . . .C.23 AVG results using IALS on Izmir's dataset . . . . . . . . . . . . . . .C.24 UUI results using IALS on Izmir's dataset . . . . . . . . . . . . . . .C.25 UUT results using IALS on Izmir's dataset . . . . . . . . . . . . . . .C.26 LM results using POP on Izmir's dataset . . . . . . . . . . . . . . . .C.27 AVM results using POP on Izmir's dataset . . . . . . . . . . . . . . .C.28 AVG results using POP on Izmir's dataset . . . . . . . . . . . . . . .C.29 UUI results using POP on Izmir's dataset . . . . . . . . . . . . . . .C.30 UUT results using POP on Izmir's dataset . . . . . . . . . . . . . . .C.31 LM results using SGD on Mexico City's dataset . . . . . . . . . . . .C.32 AVM results using SGD on Mexico City's dataset . . . . . . . . . . .C.33 AVG results using SGD on Mexico City's dataset . . . . . . . . . . .C.34 UUI results using SGD on Mexico City's dataset . . . . . . . . . . . .C.35 UUT results using SGD on Mexico City's dataset . . . . . . . . . . .C.36 LM results using IALS on Mexico City's dataset . . . . . . . . . . . .C.37 AVM results using IALS on Mexico City's dataset . . . . . . . . . . .C.38 AVG results using IALS on Mexico City's dataset . . . . . . . . . . .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".