Beauty and the Beastly Search: Finding Luxury in a Product Hierarchy
Bibliographic record
Abstract
There has been considerable research on design of menu hierarchies in general spanning several decades. However there is much less research on menus relating to specific types of product in online retail settings. Thus there is little guidance in the research literature on specific issues such as how to place luxury items within a beauty product hierarchy, which is the focus of this paper. We report on a study that addressed this problem for an ecommerce site associated with a large Canadian retailer. In a within subjects design, participants searched for four beauty-related products (two of which were classed as “luxury” items) either in a hierarchy where luxury items were intermingled with other products addressing the same need (the “Combined” condition), or in a hierarchy where there was a split between luxury and non-luxury products at the top level (the “Split” condition). Segregating luxury products in the product hierarchy was found to lead to significantly slower, and more lengthy (in terms of links traversed), searches. Searches were found to be more efficient in the “Combined” condition than the “Split” Condition both when searching for luxury items, and when searching for non-luxury items. This work has implications for existing brick-and-mortar retailers moving into or expanding e-commerce portals. Our results suggest that the separation of luxury from non-luxury items in bricks-and-mortar stores does not transfer well to online product hierarchies, where similar segregation leads to poorer digital navigation performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".