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