Initial commit — Hamkadr (همکادر) healthcare-staffing marketplace

ASP.NET Core 10 Razor Pages + PostgreSQL/EF Core. RTL Persian, Jalali dates, self-hosted Vazirmatn, teal/coral brand.

Features:
- Shift listings: browse/filter (city, district, role, type, pay), weekly Jalali calendar, detail + interest handoff, near-me distance sort
- Hiring (استخدام) listings with employment type + salary range
- Pattern-engine recommendations + anonymous interest tracking (visitor cookie)
- Heuristic Persian listing-parser + admin queue (raw channel post → shift/job)
- Phone-OTP cookie auth + visitor-history linking + profile

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

FEATURES.md

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+# Feature Catalog & Recommendation Engine
+
+Scope note: the platform now serves **all healthcare staff** — پزشک عمومی/متخصص، پرستار،
+ماما، تکنسین (اتاق عمل، بیهوشی، رادیولوژی، آزمایشگاه)، و سایر کادر درمان — not just GPs.
+The unit of value is the **shift/job**, matched to a **person's interests**.
+
+---
+
+## Part A — The interest & recommendation engine
+
+Goal: learn what each person wants and proactively surface the right shifts ("به‌جای اینکه
+کاربر دنبال شیفت بگردد، شیفت مناسب سراغ او بیاید").
+
+### What we track (signals)
+
+**Explicit (the person tells us):**
+- Role(s) + specialty/skills (پزشک، پرستار، ماما، …)
+- Preferred cities / areas / max commute distance
+- Preferred shift types (صبح/عصر/شب/آنکال) and days/times available
+- Minimum pay expectation, pay type
+- Saved searches + favorited facilities
+
+**Implicit (behavior — the gold for ML later):**
+- `InterestEvent` log: view, click, dwell-time, save, apply, dismiss, hide-facility
+- Which shifts they applied to vs ignored
+- Outcomes: accepted / rejected / completed (and did they come back)
+
+### Three-stage build (ship value early, add intelligence over time)
+
+**Stage 1 — Pattern engine (rule-based scoring).** Explainable, cold-start-safe, no ML infra.
+```
+score(shift, person) =
+    w1 · role/specialty match        (hard filter or heavy weight)
+  + w2 · city/area match
+  + w3 · shift-type preference
+  + w4 · pay vs expectation
+  + w5 · availability/day-time fit
+  + w6 · affinity (favorited / previously applied to this facility)
+  + w7 · freshness
+  − penalties (dismissed similar, hidden facility)
+```
+Produces a ranked **personalized feed** + a reason chip: «چون به شیفت‌های شب بیمارستان میلاد علاقه
+نشان دادی». Tunable weights, fully deterministic.
+
+**Stage 2 — Behavioral / collaborative.** From the `InterestEvent` matrix:
+- «کاربرانی شبیه تو این شیفت‌ها را برداشتند» (item-item / user-user collaborative filtering)
+- Implicit-feedback preference vectors; auto-learn weights instead of hand-tuning.
+
+**Stage 3 — AI / ML.**
+- **Embeddings:** encode each shift (free-text + structured) and each profile into vectors;
+  rank by similarity. Enables fuzzy/semantic matching and natural-language search
+  («شیفت شب آخر هفته نزدیک خونه با حقوق خوب»).
+- **LLM for listing-parsing:** turn messy Telegram/Bale/Divar posts into structured shifts
+  automatically (huge for the aggregation pipeline + cold-start).
+- **Learning-to-rank** trained on apply→accept outcomes.
+- **Demand forecasting:** flag shifts likely to go unfilled; suggest pay to facilities.
+
+> **Iran constraint:** OpenAI/Anthropic APIs are blocked. Plan to **self-host** a multilingual
+> embedding model + a small Persian-capable LLM on the VPS, or use an Iranian AI provider.
+> Stage 1 needs zero AI infra, so we get personalization live immediately.
+
+### How suggestions reach the person
+Personalized home feed · "پیشنهادهای ویژه شما" section · daily/instant alerts via
+**Bale bot + SMS (Kavenegar) + email** · and the reverse: suggest candidates to facilities.
+
+### New data entities this needs
+`UserPreferences`, `InterestEvent`, `SavedSearch`, `Favorite`, `AlertSubscription`,
+plus `Skill`/`Role` taxonomy.
+
+---
+
+## Part B — Full functionality catalog
+
+### 1. For job seekers (پزشک/پرستار/ماما/…)
+- Multi-role profile + skills, with **medical-council (نظام پزشکی/نظام پرستاری) verification**
+- **Document wallet** (license, certificates) — verified badge
+- Personalized recommendation feed (Part A)
+- **Availability calendar** — mark when you're free; get matched
+- Saved searches + **instant alerts** (Bale/SMS/email/push)
+- One-tap «اعلام تمایل» / apply; **application tracking** board
+- Favorites / bookmarks; hide facilities
+- **In-app chat** with facility (or contact handoff)
+- Ratings & reviews of facilities; **earnings tracker**
+- **Shift-swap marketplace** — trade shifts with other staff
+- Reliability/reputation score (shows up first to facilities)
+
+### 2. For facilities (بیمارستان/کلینیک/درمانگاه)
+- Self-serve posting + **recurring shift templates** (e.g. "every night this month")
+- **Applicant management** — shortlist, accept, reject, message
+- Reverse recommendations — «این پزشکان مناسب شیفت شما هستند»
+- **Urgent shift broadcast** (push to all matching nearby staff instantly)
+- Verified-facility badge; rate doctors
+- Analytics: fill rate, time-to-fill, no-show rate
+- Bulk import; multi-branch/department management
+
+### 3. Marketplace / platform
+- **Aggregation pipeline** from Telegram/Bale/Divar + AI parsing (Part A Stage 3)
+- Admin moderation/normalization queue
+- Trust & safety: identity & license verification, spam/fraud detection
+- Notifications hub (Bale bot, SMS, email, web push)
+- Dispute resolution / no-show handling
+- **Payments later:** subscriptions, pay-per-post, or commission/escrow (Iranian gateway:
+  Zarinpal/IDPay)
+
+### 4. AI / intelligence layer
+- Recommendation engine · natural-language search · listing auto-parsing
+- Demand forecasting + **dynamic pay suggestions** for facilities
+- Profile/resume parsing · duplicate & fraud detection on listings
+- Chatbot assistant (Bale) to search and apply by chat
+
+### 5. Growth & engagement
+- **Bale/Telegram bot** (search, alerts, apply — meets users where they already are)
+- SEO content pages per hospital / city / role (organic doctor traffic)
+- Referral program · reliability gamification · SMS/email campaigns
+
+---
+
+## Part C — Naming (decide with the user)
+Working name is **شیفت‌یاب**. Since scope is now all roles, a shift-centric, brandable name fits
+Iranian startup naming (Snapp/Tapsi/Divar/Bama style). Shortlist:
+- **شیفتو (Shifto)** — short, playful, very on-trend; strong .ir brand potential
+- **شیفت‌یار (ShiftYar)** — "shift companion/helper"; professional, clear meaning
+- **هم‌شیفت (Hamshift)** — "co-shift"; community feel
+- **کادریاب (Kadryab)** — "staff-finder"; emphasizes whole کادر درمان, not just doctors