Interaction design for mobile product recommendation agents
Bibliographic record
Abstract
Mobile product recommendation agents (RAs) are software systems that operate on mobile handheld devices, using wireless Internet to support users' decisions en route, such as consumers' product choices in retail stores. As the demand for ubiquitous access to the web grows, potential benefits of mobile RAs have been recognized, albeit with little supporting empirical evidence. We investigate whether and how mobile RAs enhance users' decisions in retail stores by reducing the effort to make purchase decisions while augmenting the accuracy of the decisions. In addition, to identify potential design principles for mobile RAs, we compare and evaluate two interaction styles of mobile RAs: alternative-driven (RA-AL) versus attribute-driven (RA-AT) interactions. The results of a laboratory experiment conducted in a simulated store indicate that mobile RAs reduced users' perceived effort and increased accuracy of their decisions. Furthermore, RA-AL users made more accurate decisions than RA-AT users due to the RA-AL's interaction style, which was compatible with the way in which users processed information and made decisions in the store. These empirical results support the notion that mobile RAs should be designed to fit the user's task undertaken in the particular context.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".