LCM-Promo
We present LCM-Promo, an LLM-based uplift model for promotion allocation. LCM-Promo is a task-specific Large Commerce Model (LCM): a finetuned LLM trained on transactional commerce data to predict user intent and promotion response. It learns monotonic promotion-response curves from production logs by interpreting single-token outputs as conversion-threshold
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- First observed in our collection
- Nov 10, 2026, 2:42 PM UTC
- Earliest cited publication
- Oct 27, 2026, 5:15 PM UTC
- Last observed
- Nov 12, 2026, 2:25 AM UTC
Evidence in the last complete seven days
- Source mentions
- 0 Previous seven days 0
- Distinct sources
- 0 Previous 0
- Comparable growth
- Not comparable
- Ranking appearances
- 2
Collection observations and undated records are separate from dated publication counts. Observed in 7d：1 · Undated records：0
Collection coverage · Comparable windowsCurrent / previous observed days 7 / 7
Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.
Current window：2026-11-05 → 2026-11-12 (end excluded) · History 24 days
| Source | Current days | Previous days | Last success |
|---|---|---|---|
| arXiv | 7 | 6 | Nov 12, 2026, 1:30 AM UTC |
| arXiv | 7 | 7 | Nov 11, 2026, 10:56 AM UTC |
| Ask HN | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| Ask HN | 7 | 7 | Nov 12, 2026, 2:15 AM UTC |
| Ask HN | 7 | 7 | Nov 12, 2026, 2:15 AM UTC |
| Ask HN | 7 | 7 | Nov 12, 2026, 2:15 AM UTC |
| GitHub | 7 | 7 | Nov 11, 2026, 5:01 PM UTC |
| GitHub Issues | 7 | 7 | Nov 11, 2026, 5:15 PM UTC |
| GitHub Releases | 7 | 7 | Nov 12, 2026, 2:15 AM UTC |
| GitHub | 7 | 7 | Nov 11, 2026, 5:01 PM UTC |
| GitHub Issues | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| GitHub Releases | 7 | 7 | Nov 11, 2026, 7:00 PM UTC |
| GitHub | 7 | 7 | Nov 11, 2026, 5:01 PM UTC |
| GitHub Issues | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| GitHub Releases | 7 | 7 | Nov 11, 2026, 7:00 PM UTC |
| GitHub Discovery | 7 | 0 | Nov 12, 2026, 2:15 AM UTC |
| GitHub Trending | 7 | 7 | Nov 11, 2026, 9:45 PM UTC |
| 7 | 7 | Nov 11, 2026, 9:15 AM UTC | |
| Hugging Face | 7 | 7 | Nov 11, 2026, 9:15 AM UTC |
| Hugging Face Spaces | 7 | 0 | Nov 12, 2026, 1:30 AM UTC |
| Hacker News | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| Hacker News | 7 | 7 | Nov 12, 2026, 2:15 AM UTC |
| Hacker News | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| Hacker News | 7 | 0 | Nov 12, 2026, 2:15 AM UTC |
| Show HN | 7 | 0 | Nov 12, 2026, 2:15 AM UTC |
| MIT News | 6 | 7 | Nov 12, 2026, 12:45 AM UTC |
| OpenAI | 7 | 7 | Nov 11, 2026, 12:30 PM UTC |
| Product Hunt | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| 量子位 | 5 | 7 | Nov 11, 2026, 12:00 PM UTC |
| OpenRouter | 7 | 0 | Nov 12, 2026, 2:30 AM UTC |
| GitHub Trending | 7 | 7 | Nov 11, 2026, 9:45 PM UTC |
| Hugging Face Models | 7 | 0 | Nov 12, 2026, 2:30 AM UTC |
| HF Daily Papers | 7 | 0 | Nov 12, 2026, 2:25 AM UTC |
| OpenRouter | 7 | 0 | Nov 12, 2026, 2:30 AM UTC |
| Product Hunt | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| SEC EDGAR | 7 | 7 | Nov 12, 2026, 12:15 AM UTC |
| Sequoia Capital | 7 | 7 | Nov 11, 2026, 7:30 AM UTC |
| Simon Willison | 7 | 0 | Nov 11, 2026, 4:30 PM UTC |
| TechCrunch | 7 | 7 | Nov 12, 2026, 12:15 AM UTC |
| Contracts Finder | 7 | 7 | Nov 11, 2026, 11:09 PM UTC |
| V2EX | 7 | 7 | Nov 12, 2026, 12:15 AM UTC |
- Counts describe monitored public sources, not the whole web or search demand.
Daily history · up to 90 days
Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.
Dotted markers indicate days with no record. They are not counted as zero mentions.
View accessible daily data
| UTC date | Mentions | Sources | Observations | Ranking appearances |
|---|---|---|---|---|
| 2026-10-27 | 1 | 1 | 0 | 0 |
| 2026-10-28 · No record | — | — | — | — |
| 2026-10-29 | 0 | 0 | 0 | 0 |
| 2026-10-30 | 0 | 0 | 0 | 0 |
| 2026-10-31 | 0 | 0 | 0 | 0 |
| 2026-11-01 | 0 | 0 | 0 | 0 |
| 2026-11-02 | 0 | 0 | 0 | 0 |
| 2026-11-03 | 0 | 0 | 0 | 0 |
| 2026-11-04 | 0 | 0 | 0 | 0 |
| 2026-11-05 | 0 | 0 | 0 | 0 |
| 2026-11-06 | 0 | 0 | 0 | 0 |
| 2026-11-07 | 0 | 0 | 0 | 0 |
| 2026-11-08 | 0 | 0 | 0 | 0 |
| 2026-11-09 | 0 | 0 | 0 | 0 |
| 2026-11-10 | 0 | 0 | 1 | 1 |
| 2026-11-11 | 0 | 0 | 0 | 1 |
| 2026-11-12 | 0 | 0 | 0 | 1 |
Source evidence
Sources in the evidence shown：HF Daily Papers 1
HF Daily Papers · Published Oct 27, 2026
LCM-Promo: An LLM-Based Uplift Model for Promotion Allocation
We present LCM-Promo, an LLM-based uplift model for promotion allocation. LCM-Promo is a task-specific Large Commerce Model (LCM): a finetuned LLM trained on transactional commerce data to predict user intent and promotion response. It learns monotonic promotion-response curves from production logs by interpreting single-token outputs as conversion-threshold
Review priority 43 / 100
This score orders research candidates. It is not search volume, a forecast or verified commercial demand.
Evidence
12.7 / 30Distinct dated articles or first observations; repeat crawling adds nothing.
Publisher diversity
8 / 25Conservative owner grouping; syndicated copies count once.
Comparable change
0 / 20Comparable complete weeks only; no percentage from a zero baseline.
Recent evidence
13.4 / 15Latest publication or first observation, not repeated fetch time.
Name specificity
8.5 / 10Explicit entities and technical concepts outrank ambiguous acronyms.
Limitations and interpretation
- First observation is a collection milestone, not proof that a word or product is new to the world.
- Counts describe monitored public sources, not the whole web or search demand.
- First observed here is not the invention date of a name or idea.
- Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
- Owner grouping is conservative; undisclosed syndication may remain.
- Repeat ranking appearances are observations, not new articles or independent endorsements.
- The previous week is too small for a reliable percentage change.
- Current evidence comes from one publisher or owner.