Building <i>k</i>‐partite association graphs for finding recommendation patterns from questionnaire data
Bibliographic record
Abstract
Abstract Graph‐pattern association rules have been explored for detecting frequent subgraph structures in real‐world network data, which can reveal new insights for decision‐making, recommender systems, and predictive models. However, questionnaire data have been neglected so far even though they are one of the most affordable ways to gather quantitative data. Questionnaires can cover every aspect of a topic, generating new strategies and trends for many organisations. The challenge is twofold: develop a model for handling nominal/Boolean data and ordinal data simultaneously, as well as multiple values assigned to a single item. In this article, the synergy between the well‐known Apriori algorithm and k‐partite graph modelling is proposed to discover frequent recommendation patterns from questionnaire data. Using graph centrality and similarity measures, the large number of association rules are further analysed to discover meaningful spatial structures in non‐metric spaces. Counting triangles is also used to uncover hidden thematic structures of link recommendations. We demonstrate how our proposed approach can be applied to a tourism questionnaire survey to reveal frequent patterns in k‐partite graphs, which can further be used for recommender systems.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".