Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research
Bibliographic record
Abstract
The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".