Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2401263343
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xiaoyi Zen (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Training Effectiveness: 1 paper
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Firm Revenue: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Using large language models to generate and clean training interactions raises e-commerce recommendation recall by 6.02% and gross merchandise value by 1.22%, mainly by improving long-tail item recommendations; the approach boosts I2I systems without changing model architectures.arxiv | Xiaoyi Zen provider id |
2025-12-25 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.