Add planning docs for Nostr place reviews
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doc/nostr/nip-place-reviews.md
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doc/nostr/nip-place-reviews.md
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# NIP-XX: Place Reviews
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## Abstract
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This NIP defines a standardized event format for decentralized place reviews using Nostr. Reviews are tied to real-world locations (e.g. OpenStreetMap POIs) via tags, and include structured, multi-aspect ratings, a binary recommendation signal, and optional contextual metadata.
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The design prioritizes:
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* Small event size
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* Interoperability across clients
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* Flexibility for different place types
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* Efficient geospatial querying using geohashes
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---
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## Event Kind
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`kind: 30315` (suggested; subject to coordination)
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---
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## Tags
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Additional tags MAY be included by clients but are not defined by this specification.
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This NIP reuses and builds upon existing Nostr tag conventions:
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* `i` tag: see NIP-73 (External Content Identifiers)
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* `g` tag: geohash-based geotagging (community conventions)
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Where conflicts arise, this NIP specifies the behavior for review events.
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### Required
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#### `i` — Entity Identifier
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Identifies the reviewed place using an external identifier. OpenStreetMap data is the default:
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```
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["i", "osm:<type>:<id>"]
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```
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Requirements:
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* For OSM POIs, `<type>` MUST be one of: `node`, `way`, `relation`
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Examples:
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```
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["i", "osm:node:123456"]
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["i", "osm:way:987654"]
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```
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---
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### Geospatial Tags
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#### `g` — Geohash
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Geohash tags are used for spatial indexing and discovery.
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##### Requirements
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* Clients MUST include at least one high-precision geohash (length ≥ 9)
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##### Recommendations
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Clients SHOULD include geohashes at the following resolutions:
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* length 4 — coarse (city-scale discovery)
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* length 6 — medium (default query level, ~1 km)
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* length 7 — fine (neighborhood, ~150 m)
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Example:
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```
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["g", "thrr"]
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["g", "thrrn5"]
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["g", "thrrn5k"]
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["g", "thrrn5kxyz"]
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```
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##### Querying
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Geospatial queries are performed using the `g` tag.
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* Clients SHOULD query using a single geohash precision level per request
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* Clients MAY include multiple geohash values in a filter to cover a bounding box
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* Clients SHOULD limit the number of geohash values per query (e.g. ≤ 30)
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* Clients MAY reduce precision or split queries when necessary
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Note: Other queries (e.g. fetching reviews for a specific place) are performed using the `i` tag and are outside the scope of geospatial querying.
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---
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## Content (JSON)
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The event `content` MUST be valid JSON matching the following schema.
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### Schema
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```json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"type": "object",
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"required": ["version", "ratings"],
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"additionalProperties": false,
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"properties": {
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"version": {
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"type": "integer",
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"const": 1
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},
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"ratings": {
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"type": "object",
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"required": ["quality"],
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"additionalProperties": false,
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"properties": {
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"quality": { "$ref": "#/$defs/score" },
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"value": { "$ref": "#/$defs/score" },
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"experience": { "$ref": "#/$defs/score" },
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"accessibility": { "$ref": "#/$defs/score" },
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"aspects": {
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"type": "object",
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"minProperties": 1,
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"maxProperties": 20,
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"additionalProperties": { "$ref": "#/$defs/score" },
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"propertyNames": {
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"pattern": "^[a-z][a-z0-9_]{1,31}$"
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}
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}
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}
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},
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"recommend": {
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"type": "boolean"
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},
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"familiarity": {
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"type": "string",
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"enum": ["low", "medium", "high"],
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"description": "User familiarity: low = first visit; medium = occasional; high = frequent"
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},
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"context": {
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"type": "object",
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"additionalProperties": false,
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"properties": {
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"visited_at": {
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"type": "integer",
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"minimum": 0
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},
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"duration_minutes": {
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"type": "integer",
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"minimum": 0,
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"maximum": 1440
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},
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"party_size": {
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"type": "integer",
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"minimum": 1,
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"maximum": 100
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}
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}
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},
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"review": {
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"type": "object",
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"additionalProperties": false,
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"properties": {
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"text": {
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"type": "string",
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"maxLength": 1000
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},
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"language": {
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"type": "string",
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"pattern": "^[a-z]{2}(-[A-Z]{2})?$"
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}
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}
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}
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},
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"$defs": {
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"score": {
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"type": "integer",
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"minimum": 1,
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"maximum": 10
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}
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}
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}
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```
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---
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## Example
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### Restaurant Review Event
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#### Tags
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```
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[
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["i", "osm:node:123456"],
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["g", "thrr"],
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["g", "thrrn5"],
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["g", "thrrn5k"],
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["g", "thrrn5kxyz"]
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]
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```
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#### Content
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```json
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{
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"version": 1,
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"ratings": {
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"quality": 9,
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"value": 8,
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"experience": 9,
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"accessibility": 7,
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"aspects": {
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"food": 9,
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"service": 6,
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"ambience": 8,
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"wait_time": 5
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}
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},
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"recommend": true,
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"familiarity": "medium",
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"context": {
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"visited_at": 1713200000,
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"duration_minutes": 90,
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"party_size": 2
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},
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"review": {
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"text": "Excellent food with bold flavors. Service was a bit slow, but the atmosphere made up for it.",
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"language": "en"
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}
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}
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```
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---
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## Semantics
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### Ratings
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* Scores are integers from 1 to 10
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* `quality` is required and represents the core evaluation of the place
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* Other fields are optional and context-dependent
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### Aspects
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* Free-form keys allow domain-specific ratings
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* Clients MAY define and interpret aspect keys
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* Clients SHOULD reuse commonly established aspect keys where possible
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---
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## Recommendation Signal
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The `recommend` field represents a binary verdict:
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* `true` → user recommends the place
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* `false` → user does not recommend the place
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Clients SHOULD strongly encourage users to provide this value.
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---
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## Familiarity
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Represents user familiarity with the place:
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* `low` → first visit or limited exposure
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* `medium` → occasional visits
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* `high` → frequent or expert-level familiarity
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Clients MAY use this signal for weighting during aggregation.
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---
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## Context
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Optional metadata about the visit.
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* `visited_at` is a Unix timestamp
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* `duration_minutes` represents time spent
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* `party_size` indicates group size
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---
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## Interoperability
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This specification defines a content payload only.
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* In Nostr: place identity is conveyed via tags
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* In other protocols (e.g. ActivityPub, AT Protocol): identity MUST be mapped to the equivalent field (e.g. `object`)
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Content payloads SHOULD NOT include place identifiers.
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---
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## Rationale
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### No Place Field in Content
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Avoids duplication and inconsistency with tags.
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### Multi-Aspect Ratings
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Separates concerns (e.g. quality vs service), improving signal quality.
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### Recommendation vs Score
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Binary recommendation avoids averaging pitfalls and improves ranking.
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### Familiarity
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Provides a human-friendly proxy for confidence without requiring numeric input.
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### Geohash Strategy
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Multiple resolutions balance:
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* efficient querying
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* small event size
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* early-stage discoverability
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---
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## Future Work
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* Standardized aspect vocabularies
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* Reputation and weighting models
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* Indexing/aggregation services
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* Cross-protocol mappings
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---
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## Security Considerations
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* Clients SHOULD validate all input
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* Malicious or spam reviews may require external moderation or reputation systems
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---
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## Copyright
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This NIP is public domain.
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6
doc/nostr/notes.md
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6
doc/nostr/notes.md
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# Notes
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- NIP-73 for external IDs ("osm:node:123456"): https://github.com/nostr-protocol/nips/blob/744bce8fcae0aca07b936b6662db635c8b4253dd/73.md
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- Places NIP-XX draft PR: https://github.com/nostr-protocol/nips/pull/927
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- NPM package for generating multi-resolution geotags: https://sandwichfarm.github.io/nostr-geotags/#md:nostr-geotags
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- AppleSauce docs for AI agents: https://applesauce.build/introduction/mcp-server.html
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251
doc/nostr/ranking.md
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doc/nostr/ranking.md
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# Ranking Algorithm
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Your inputs:
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* many users
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* partial ratings
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* different priorities
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Your output:
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> “Best place *for this user right now*”
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---
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## Step 1: Normalize scores
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Convert 1–10 → 0–1:
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```text
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normalized_score = (score - 1) / 9
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```
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Why:
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* easier math
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* comparable across aspects
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---
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## Step 2: Per-aspect aggregation (avoid averages trap)
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Instead of mean, compute:
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### A. Positive ratio
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```text
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positive = score >= 7
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negative = score <= 4
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```
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Then:
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```text
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positive_ratio = positive_votes / total_votes
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```
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---
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### B. Confidence-weighted score
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Use something like a **Wilson score interval** (this is key):
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* prevents small-sample abuse
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* avoids “1 review = #1 place”
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---
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## Step 3: Build aspect scores
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For each aspect:
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```text
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aspect_score = f(
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positive_ratio,
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confidence,
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number_of_reviews
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)
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```
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You can approximate with:
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```text
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aspect_score = positive_ratio * log(1 + review_count)
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```
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(Simple, works surprisingly well)
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---
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## Step 4: User preference weighting
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User defines:
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```json
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{
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"quality": 0.5,
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"value": 0.2,
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"service": 0.2,
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"speed": 0.1
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}
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```
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Then:
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```text
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final_score = Σ (aspect_score × weight)
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```
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---
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## Step 5: Context filtering (this is your unfair advantage)
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Filter reviews before scoring:
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* time-based:
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* “last 6 months”
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* context-based:
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* lunch vs dinner
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* solo vs group
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This is something centralized platforms barely do.
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---
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## Step 6: Reviewer weighting (later, but powerful)
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Weight reviews by:
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* consistency
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* similarity to user preferences
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* past agreement
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This gives you:
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> “people like you liked this”
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---
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# 3. Example end-to-end
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### Raw reviews:
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| User | Food | Service |
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| ---- | ---- | ------- |
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| A | 9 | 4 |
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| B | 8 | 5 |
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| C | 10 | 3 |
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---
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### Derived:
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* food → high positive ratio (~100%)
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* service → low (~33%)
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---
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### User preferences:
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```json
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{
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"food": 0.8,
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"service": 0.2
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}
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```
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→ ranks high
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Another user:
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```json
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{
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"food": 0.3,
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"service": 0.7
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}
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```
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→ ranks low
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👉 Same data, different truth
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That’s your killer feature.
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||||
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---
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# 4. Critical design choices (don’t skip these)
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## A. No global score in protocol
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Let clients compute it.
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---
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||||
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## B. Embrace incomplete data
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||||
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Most reviews will have:
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* 1–3 aspects only
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That’s fine.
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||||
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||||
---
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||||
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||||
## C. Time decay (important)
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||||
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Recent reviews should matter more:
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```text
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weight = e^(-λ × age)
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||||
```
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||||
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||||
---
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||||
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## D. Anti-gaming baseline
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||||
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Even in nostr:
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||||
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||||
* spam will happen
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Mitigation later:
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||||
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||||
* require minimum interactions
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||||
* reputation layers
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||||
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||||
---
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||||
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# 5. What you’ve built (zooming out)
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||||
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||||
This is not a review system.
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||||
It’s:
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||||
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||||
> A decentralized, multi-dimensional reputation graph for real-world places
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||||
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||||
That’s much bigger.
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||||
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||||
---
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||||
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||||
# 6. Next step (if you want to go deeper)
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||||
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||||
We can design:
|
||||
|
||||
### A. Query layer
|
||||
|
||||
* how clients fetch & merge nostr reviews efficiently
|
||||
|
||||
### B. Anti-spam / trust model
|
||||
|
||||
* web-of-trust
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||||
* staking / reputation
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||||
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||||
### C. OSM integration details
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||||
|
||||
* handling duplicates
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||||
* POI identity conflicts
|
||||
|
||||
---
|
||||
|
||||
If I had to pick one next:
|
||||
👉 **trust/reputation system** — because without it, everything you built *will* get gamed.
|
||||
101
doc/nostr/ratings.md
Normal file
101
doc/nostr/ratings.md
Normal file
@@ -0,0 +1,101 @@
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# Canonical Aspect Vocabulary (v0.1)
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||||
|
||||
## A. Core universal aspects
|
||||
|
||||
These should work for *any* place:
|
||||
|
||||
```json
|
||||
[
|
||||
"quality", // core offering (food, repair, exhibits, etc.)
|
||||
"value", // value for money/time
|
||||
"experience", // comfort, usability, vibe
|
||||
"accessibility" // ease of access, inclusivity
|
||||
]
|
||||
```
|
||||
|
||||
### Why these work
|
||||
|
||||
* **quality** → your “product” abstraction (critical)
|
||||
* **value** → universally meaningful signal
|
||||
* **experience** → captures everything “soft”
|
||||
* **accessibility** → often ignored but high utility
|
||||
|
||||
👉 Resist adding more. Every extra “universal” weakens the concept.
|
||||
|
||||
---
|
||||
|
||||
## B. Common cross-domain aspects (recommended pool)
|
||||
|
||||
Not universal, but widely reusable:
|
||||
|
||||
```json
|
||||
[
|
||||
"service", // human interaction
|
||||
"speed", // waiting time / turnaround
|
||||
"cleanliness",
|
||||
"safety",
|
||||
"reliability",
|
||||
"atmosphere"
|
||||
]
|
||||
```
|
||||
|
||||
These apply to:
|
||||
|
||||
* restaurants, garages, clinics, parks, etc.
|
||||
|
||||
---
|
||||
|
||||
## C. Domain-specific examples (NOT standardized)
|
||||
|
||||
Let clients define freely:
|
||||
|
||||
```json
|
||||
{
|
||||
"restaurant": ["food", "drinks"],
|
||||
"bar": ["drinks", "music"],
|
||||
"garage": ["work_quality", "honesty"],
|
||||
"park": ["greenery", "amenities"],
|
||||
"museum": ["exhibits", "crowding"]
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## D. Key rule (this prevents chaos)
|
||||
|
||||
👉 **Aspect keys MUST be lowercase snake_case**
|
||||
|
||||
👉 **Meaning is defined socially, not technically**
|
||||
|
||||
To reduce fragmentation:
|
||||
|
||||
* publish a **public registry (GitHub repo)**
|
||||
* clients can:
|
||||
|
||||
* suggest additions
|
||||
* map synonyms
|
||||
|
||||
---
|
||||
|
||||
## E. Optional normalization hint (important later)
|
||||
|
||||
Allow this:
|
||||
|
||||
```json
|
||||
"aspect_aliases": {
|
||||
"food": "quality",
|
||||
"work_quality": "quality"
|
||||
}
|
||||
```
|
||||
|
||||
Not required, but useful for aggregation engines.
|
||||
|
||||
---
|
||||
|
||||
## Notes
|
||||
|
||||
Map familiarity in UI to:
|
||||
|
||||
* high: “I know this place well”
|
||||
* medium: “Been a few times”
|
||||
* low: “First visit”
|
||||
92
doc/nostr/review-schema.json
Normal file
92
doc/nostr/review-schema.json
Normal file
@@ -0,0 +1,92 @@
|
||||
{
|
||||
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
||||
"$id": "https://example.org/schemas/place-review.json",
|
||||
"title": "Decentralized Place Review (Nostr/Event Content)",
|
||||
"type": "object",
|
||||
"required": ["version", "place", "ratings"],
|
||||
"additionalProperties": false,
|
||||
|
||||
"properties": {
|
||||
"version": {
|
||||
"type": "integer",
|
||||
"const": 1
|
||||
},
|
||||
|
||||
"ratings": {
|
||||
"type": "object",
|
||||
"required": ["quality"],
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"quality": { "$ref": "#/$defs/score" },
|
||||
"value": { "$ref": "#/$defs/score" },
|
||||
"experience": { "$ref": "#/$defs/score" },
|
||||
"accessibility": { "$ref": "#/$defs/score" },
|
||||
|
||||
"aspects": {
|
||||
"type": "object",
|
||||
"minProperties": 1,
|
||||
"maxProperties": 20,
|
||||
"additionalProperties": { "$ref": "#/$defs/score" },
|
||||
"propertyNames": {
|
||||
"pattern": "^[a-z][a-z0-9_]{1,31}$"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"recommend": {
|
||||
"type": "boolean",
|
||||
"description": "Whether the user recommends this place to others"
|
||||
},
|
||||
|
||||
"familiarity": {
|
||||
"type": "string",
|
||||
"enum": ["low", "medium", "high"],
|
||||
"description": "User familiarity with the place. Suggested interpretation: 'low' = first visit or very limited experience; 'medium' = visited a few times or moderate familiarity; 'high' = frequent visitor or strong familiarity."
|
||||
},
|
||||
|
||||
"context": {
|
||||
"type": "object",
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"visited_at": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"duration_minutes": {
|
||||
"type": "integer",
|
||||
"minimum": 0,
|
||||
"maximum": 1440
|
||||
},
|
||||
"party_size": {
|
||||
"type": "integer",
|
||||
"minimum": 1,
|
||||
"maximum": 100
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"review": {
|
||||
"type": "object",
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string",
|
||||
"maxLength": 1000
|
||||
},
|
||||
"language": {
|
||||
"type": "string",
|
||||
"pattern": "^[a-z]{2}(-[A-Z]{2})?$"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"$defs": {
|
||||
"score": {
|
||||
"type": "integer",
|
||||
"minimum": 1,
|
||||
"maximum": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user