Readwise Recommendation Engine

A standalone Node.js recommendation system that surfaces relevant articles from your Readwise queue based on your reading interests.

Overview

This system analyzes your reading interests and generates daily HTML recommendations from your Readwise "Later" queue and tagged collections. It scores articles based on thematic keyword matching, filters out previously recommended items, and hosts the results as a bookmarkable webpage.

Claude Code is optional — it can help create your interest profile by analyzing your highlights, but you can also create the profile manually.

Prerequisites

Node.js 14+
Readwise account with API access
Readwise API token

Optional (for enhanced features):

Readwise CLI (npm install -g @readwise/cli) — required for qmd engine exports and discovery search
qmd — required only for the qmd scoring engine (BM25+vector full-text search)

Setup

1. Clone and Install

git clone https://github.com/derekvan/Readwise-recommender.git
cd Readwise-recommender
npm install

2. Set Your Readwise API Token

export READWISE_TOKEN="your-api-token-here"

Add this to your ~/.bashrc or ~/.zshrc to make it permanent.

3. (Optional) Install the Readwise CLI

The Readwise CLI is required for the qmd scoring engine and discovery search. It is not needed for the default keyword scoring workflow.

npm install -g @readwise/cli
readwise login-with-token your-readwise-token

Verify it works:

readwise reader-list-documents --location later --limit 1 --json

4. Create Your Interest Profile

Your interest profile defines the topics you care about. There are two ways to create it:

Option A: Use Claude Code (Recommended)

Claude Code can analyze your Readwise highlights and automatically generate a high-quality interest profile. This approach identifies thematic patterns across thousands of highlights, picking keywords and weights you might not think of.

Step 1: Fetch your highlights

Fetch all my Readwise highlights using readwise_export_highlights
and save to data/highlights_raw.json

Step 2: Generate interest profile

Analyze all highlights in data/highlights_raw.json. Identify 25 thematic clusters
representing my core interests. For each cluster, provide: theme name, 5-10 keywords,
and calculate weight based on how many highlights match. Save to data/interest_profile.json.

Claude Code will produce a JSON file with your personalized reading themes.

Option B: Create Manually

Create data/interest_profile.json with this structure:

{
  "version": "1.0",
  "metadata": {
    "created_at": "2026-01-29T10:00:00Z",
    "cluster_count": 15
  },
  "clusters": [
    {
      "id": "cluster_001",
      "theme": "Decision Making & Cognitive Frameworks",
      "keywords": ["decision-making", "intuition", "biases", "rationality", "thinking"],
      "weight": 0.18,
      "description": "Frameworks for making better decisions and understanding cognitive biases"
    },
    {
      "id": "cluster_002",
      "theme": "Writing & Communication",
      "keywords": ["writing", "storytelling", "editing", "clarity", "communication"],
      "weight": 0.12,
      "description": "Techniques for clear, effective writing and communication"
    },
    {
      "id": "cluster_003",
      "theme": "Productivity & Focus",
      "keywords": ["productivity", "focus", "deep work", "distraction", "habits"],
      "weight": 0.15,
      "description": "Systems for managing attention and building effective habits"
    }
  ]
}

Field guide:

theme: A topic you care about (e.g., "Decision Making")
keywords: Words that signal this topic in article titles/summaries (lowercase, 5-10 per theme)
weight: Importance of this theme (0.0-1.0). Higher = more likely to surface articles on this topic. Weights don't need to sum to 1.0.
description: Human-readable explanation of what this cluster represents

Tips:

Start with 10-25 themes that represent your core reading interests
Think about what words appear in article titles/summaries for each topic
Higher weights prioritize that theme in scoring
You can refine your profile over time based on which recommendations feel right

5. Configure Settings

Copy the example settings file:

cp utils/settings.example.json utils/settings.json

Edit utils/settings.json to customize:

{
  "tagBucket": {
    "enabled": true,
    "tag": "bankruptcy2025-12",
    "label": "Bankruptcy Readings",
    "emoji": "💼",
    "cooldownMonths": 6,
    "count": 3
  },
  "laterBucket": {
    "label": "Top Picks",
    "emoji": "✨",
    "cooldownMonths": 2,
    "count": 2
  },
  "batchCount": 3,
  "recommendationsPerBucket": 5,
  "excludedCategories": ["pdf", "epub"],
  "upload": {
    "method": "local"
  }
}

See Configuration Reference below for all options.

6. Fetch Initial Data

Choose a scoring engine path:

Keyword engine (default) — REST API with full-text:

node utils/fetch_later_incremental.js   # fetches full HTML content (~several minutes)
node utils/merge_chunks.js              # strips HTML → plain text, saves fulltext map
node utils/build_scored_cache.js        # scores documents (uses full text if available)

QMD engine — Readwise Export with full-text:

node utils/export_docs_to_qmd.js        # exports Later docs as markdown, indexes via qmd
node utils/build_scored_cache.js        # scores documents via BM25 search

(Optional) Fetch from a tagged collection:

# Keyword engine
node utils/fetch_tag_incremental.js "your-tag"
node utils/merge_tag_chunks.js "your-tag"
node utils/score_tag_documents.js "your-tag"

# QMD engine
node utils/export_tag_to_qmd.js "your-tag"
node utils/score_tag_documents.js "your-tag"

7. Generate Your First Recommendations

node daily_recs.js

This generates an HTML file at output/recommendations/YYYY-MM-DD.html and prints the file path.

Daily Usage

Once setup is complete, run one command each day:

node daily_recs.js

What this does:

Generates HTML recommendations from cached, scored data
Filters out previously recommended items
Creates multiple batches of recommendations (default: 3 batches with 2 "Later" + 3 tagged items each)
Shows batch 1 by default with a "Get More Recommendations" button to reveal additional batches
Uploads based on your configured method (or saves locally)
Prints the URL or file path

Time: < 10 seconds

The generated page includes:

Multiple batches of recommendations with progressive disclosure
Scores, matched themes, and reading time estimates
Archive buttons to remove articles from your queue without reading
Mobile-friendly, bookmarkable design

Refreshing Cached Data

The system caches scored documents to keep daily runs fast. Refresh when you've added new articles:

Refresh "Later" Documents (keyword engine):

node utils/fetch_later_incremental.js
node utils/merge_chunks.js
node utils/build_scored_cache.js

Refresh "Later" Documents (qmd engine):

node utils/export_docs_to_qmd.js    # incremental by default; use --full to force full export
node utils/build_scored_cache.js

When the qmd engine is enabled, daily_recs.js runs an incremental export automatically at startup — you usually don't need to refresh manually.

Refresh Tagged Collection (keyword engine):

node utils/fetch_tag_incremental.js "your-tag-name"
node utils/merge_tag_chunks.js "your-tag-name"
node utils/score_tag_documents.js "your-tag-name"

Refresh Tagged Collection (qmd engine):

node utils/export_tag_to_qmd.js "your-tag-name"
node utils/score_tag_documents.js "your-tag-name"

Recommendation: Refresh weekly, or whenever you've added 50+ new articles.

Configuration Reference

Edit utils/settings.json to customize behavior:

{
  "tagBucket": {
    "enabled": true,              // Enable/disable tag-based recommendations
    "tag": "bankruptcy2025-12",   // Readwise tag to track
    "label": "Bankruptcy Readings", // Display name in HTML
    "emoji": "💼",                // Section emoji
    "cooldownMonths": 6,          // Months before re-recommending same item
    "count": 3                    // Items per batch from this bucket (default: 3)
  },
  "laterBucket": {
    "label": "Top Picks",         // Display name for "Later" recommendations (default: "Top Picks")
    "emoji": "✨",
    "cooldownMonths": 2,
    "count": 2                    // Items per batch from this bucket (default: 2)
  },
  "batchCount": 3,                // Number of batches to generate (default: 3)
  "recommendationsPerBucket": 5,  // Fallback count if bucket.count not specified
  "excludedCategories": ["pdf", "epub"], // Document types to skip
  "scoring": {
    "engine": "keyword",            // "keyword" (default) or "qmd" (BM25+vector)
    "strongestThemeMultiplier": 2,  // Boost for strongest theme match
    "rawScoreMultiplier": 6,        // Overall score scaling
    "maxMatchedClusters": 5,        // Max themes shown per document
    "fullTextScoring": {
      "enabled": true,              // Use full article body in scoring (keyword engine)
      "titleWeight": 3,             // Relative weight for title matches
      "summaryWeight": 2,           // Relative weight for summary matches
      "contentWeight": 1            // Relative weight for body text matches
    }
  },
  "cache": {
    "refreshIntervalDays": 7,     // Days between full cache refreshes
    "maxIncrementalUpdates": 10   // Max incremental updates before full refresh
  },
  "upload": {
    "method": "local",            // local, github-pages, or scp
    "remoteFilename": "recs.html",
    "urlPath": "/recommendations/"
  }
}

Note: utils/settings.json is gitignored — your settings stay private.

Hosting Options

Choose how to view your daily recommendations:

Option 1: Local File (Easiest)

Open the HTML file directly in your browser:

open output/recommendations/$(date +%Y-%m-%d).html

Pros: Zero setup, complete privacy, works offline Cons: Not bookmarkable, need to find file each day

Set "upload.method": "local" in settings.json.

Option 2: GitHub Pages (Free Bookmarkable URL)

Host on GitHub Pages for a consistent, bookmarkable URL.

Initial Setup:

Create a public GitHub repository (e.g., my-recommendations)

Clone it locally and create the docs folder:

git clone https://github.com/yourusername/my-recommendations.git
cd my-recommendations
mkdir docs
echo "# My Recommendations" > docs/README.md
git add docs/ && git commit -m "Initialize docs" && git push

Enable GitHub Pages:
- Go to repository Settings → Pages
- Source: Deploy from a branch
- Branch: main → /docs folder → Save
- Your site: https://yourusername.github.io/my-recommendations/

Configure settings.json:

{
  "upload": {
    "method": "github-pages",
    "pagesRepoPath": "/absolute/path/to/my-recommendations",
    "pagesUrl": "https://yourusername.github.io/my-recommendations/"
  }
}

Daily Publishing:

node daily_recs.js automatically copies the HTML to docs/index.html, commits, and pushes.

Pros: Free hosting, bookmarkable, archive buttons work from anywhere Cons: Repository must be public (article titles visible), requires git push per update

Security Note: HTML contains article titles and reading times. If this is sensitive, use local hosting instead.

Option 3: Personal Web Server (SCP)

Upload to your own server via SCP.

Setup:

Configure SCP in utils/config.json:

{
  "sshHost": "your.server.com",
  "sshUser": "username",
  "remotePath": "/path/to/webroot/",
  "remoteFilename": "recs.html",
  "urlPath": "/recommendations/"
}

Set upload method in utils/settings.json:

{
  "upload": {
    "method": "scp"
  }
}

Ensure SSH key authentication is configured (no password prompts).

Pros: Complete privacy, custom domain, full control Cons: Requires web server with SSH access

Option 4: No Upload (Local Archive Only)

Generate HTML without uploading:

{
  "upload": {
    "method": "local"
  }
}

node daily_recs.js prints the local file path without uploading.

How Scoring Works

Hybrid Scoring Algorithm

For each document:

Extract text: title + summary + author (and full article body if available)
Match keywords: For each theme, count keyword matches across text fields
Calculate theme contribution: (matches / total_keywords) × theme_weight
Apply hybrid scoring: (strongest_theme × 2) + sum_of_other_contributions
Scale to 1-10: Multiply by 6 and cap at 10

Why hybrid? This prioritizes depth (strong single-theme match) over breadth (many weak matches).

Example:

Document A: Strong "Meditation" match (0.045) + weak matches → Score: 2.5
Document B: Many weak matches across 5 themes → Score: 2.0

Document A wins because it strongly matches a high-weight theme, even though Document B matches more themes weakly.

Scoring Engines

Two scoring engines are available. Switch with scoring.engine in utils/settings.json.

Keyword Engine (default)

Scores documents via substring matching against their text fields. If full-text content is available (fetched via REST API), it uses weighted scoring across three fields:

Field	Default Weight
Title	3×
Summary + author	2×
Article body	1×

Full-text scoring catches articles whose summaries are vague but whose body is highly relevant. Title matches still count most.

How to get full text (keyword engine):

node utils/fetch_later_incremental.js   # fetches full HTML via REST API
node utils/merge_chunks.js              # strips HTML → plain text, saves fulltext map
node utils/build_scored_cache.js        # scores with full text if available

Gotchas:

The CLI fetch (fetch_later_cli.js) does not return article body — metadata only
Daily incremental updates (update_cache_from_yesterday.js) do not fetch full text; new docs added between full rebuilds are scored on title+summary only
Full-text scoring only takes effect during a full build_scored_cache.js rebuild

Configure weights in utils/settings.json:

"scoring": {
  "fullTextScoring": {
    "enabled": true,
    "titleWeight": 3,
    "summaryWeight": 2,
    "contentWeight": 1
  }
}

QMD Engine

Scores documents by running BM25 searches against a local qmd index of full markdown documents. This is richer than substring matching: it uses proper term frequency weighting and indexes the entire article body by default.

Set "engine": "qmd" in utils/settings.json to enable:

"scoring": {
  "engine": "qmd"
}

See QMD Engine Setup below for installation and configuration.

QMD Engine Setup

Prerequisites

Install the Readwise CLI:

npm install -g @readwise/cli
readwise login-with-token your-readwise-token

Install qmd and initialize:

# Follow qmd installation instructions for your platform
qmd init

Configure qmd collections in ~/.config/qmd/index.yml:

collections:
  readwise-docs:
    path: /absolute/path/to/Readwise-recommender/data/qmd-docs
  readwise-tag-docs:
    path: /absolute/path/to/Readwise-recommender/data/qmd-tag-docs

Initial Export

Export your Later queue as markdown and index it:

node utils/export_docs_to_qmd.js        # exports docs, then runs qmd update + qmd embed

For tagged collections:

node utils/export_tag_to_qmd.js "your-tag"

Both scripts automatically run qmd update and qmd embed after export.

Daily Sync

When "engine": "qmd" is set, daily_recs.js automatically runs an incremental export at startup (Step 0) before generating recommendations. You generally don't need to export manually each day.

Force a full (non-incremental) export when needed:

node utils/export_docs_to_qmd.js --full

Known Limitation

Articles originally saved via Readwise's internal read.readwise.io URLs are not included in the export ZIP. The export script automatically backfills these from the REST API fulltext JSON (data/documents_later_fulltext.json) if it exists. Run a full REST API fetch first if you're missing documents:

node utils/fetch_later_incremental.js
node utils/merge_chunks.js

Automating with Cron

Run recommendations automatically each morning using cron.

Setup Instructions

Find your node path:

which node
# Example output: /usr/local/bin/node or /opt/homebrew/bin/node

Get your project's absolute path:

cd /path/to/Readwise-recommender
pwd
# Example output: /Users/yourusername/Code/Readwise-recommender

Set READWISE_TOKEN permanently:

Add to your shell profile (~/.bashrc, ~/.zshrc, or ~/.bash_profile):
```
export READWISE_TOKEN="your-api-token-here"
```
Then reload:
```
source ~/.zshrc  # or ~/.bashrc
```
Edit crontab:
```
crontab -e
```
Add cron job:

For 6:00 AM daily (replace paths with your actual paths):
```
0 6 * * * cd /Users/yourusername/Code/Readwise-recommender && /usr/local/bin/node daily_recs.js >> /tmp/daily_recs.log 2>&1
```
Cron time format: minute hour day month weekday
- 0 6 * * * = 6:00 AM daily
- 0 9 * * * = 9:00 AM daily
- 30 7 * * * = 7:30 AM daily
Verify cron is running:
```
crontab -l  # List your cron jobs
```

Check logs:

tail -f /tmp/daily_recs.log  # Watch output in real-time
cat /tmp/daily_recs.log      # View full log

Common Issues

Cron job not running?

Verify cron service is running: sudo launchctl list | grep cron (macOS)
Check system logs: grep CRON /var/log/syslog (Linux)
Ensure full paths are used (no ~ shorthand)

Environment variables not available?

Cron runs with minimal environment. Export READWISE_TOKEN in your shell profile as shown above
Test the exact cron command manually: cd /path && /usr/local/bin/node daily_recs.js

Wrong node version?

Cron may use system node instead of nvm/asdf node
Use full path from which node to ensure correct version

Alternative: Using launchd (macOS)

For more reliable scheduling on macOS, consider using launchd instead of cron. Create ~/Library/LaunchAgents/com.user.readwise-recs.plist:

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.user.readwise-recs</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/local/bin/node</string>
        <string>/Users/yourusername/Code/Readwise-recommender/daily_recs.js</string>
    </array>
    <key>EnvironmentVariables</key>
    <dict>
        <key>READWISE_TOKEN</key>
        <string>your-api-token-here</string>
    </dict>
    <key>StartCalendarInterval</key>
    <dict>
        <key>Hour</key>
        <integer>6</integer>
        <key>Minute</key>
        <integer>0</integer>
    </dict>
    <key>StandardOutPath</key>
    <string>/tmp/readwise-recs.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/readwise-recs-error.log</string>
</dict>
</plist>

Load it:

launchctl load ~/Library/LaunchAgents/com.user.readwise-recs.plist

Check status:

launchctl list | grep readwise-recs

Data Schemas

Interest Profile (`data/interest_profile.json`)

{
  "version": "1.0",
  "metadata": {
    "created_at": "2026-01-29T10:00:00Z",
    "cluster_count": 25
  },
  "clusters": [
    {
      "id": "cluster_001",
      "theme": "Decision Making & Cognitive Frameworks",
      "keywords": ["decision-making", "intuition", "fear", "reason", "biases"],
      "weight": 0.15,
      "description": "Frameworks for making better decisions..."
    }
  ]
}

Recommendation Log (`data/recommendation_log.json`)

{
  "version": "1.0",
  "recommendations": [
    {
      "date": "2026-01-29",
      "bucket_1_later": [
        {
          "document_id": "01kdnmypxhaphr6be3vv7xkgn4",
          "title": "How Can I Stop Being So Afraid of Changing My Life?",
          "score": 10.0,
          "themes": ["Decision Making", "Overcoming Fear"]
        }
      ],
      "bucket_2_bankruptcy": [...],
      "bonus_doc_ids": ["01xyz...", "01abc..."],
      "bonus_buckets": {
        "bucket_1_later": ["01xyz..."],
        "bucket_2_bankruptcy": ["01abc..."]
      }
    }
  ],
  "recommended_doc_ids": ["01kdnmypxhaphr6be3vv7xkgn4", ...]
}

Troubleshooting

No high-scoring recommendations?

Check your interest profile (data/interest_profile.json) — ensure keywords match how articles describe topics
Lower the threshold or refine keyword lists based on article titles in your queue

Recommendations not aligned with interests?

Review cluster weights in data/interest_profile.json
Add missing keywords that appear in article titles you're interested in
If using Claude Code, regenerate the profile after accumulating new highlights

Want more variety?

Increase batchCount in settings.json to generate more batches (e.g., change from 3 to 5)
Adjust laterBucket.count and tagBucket.count for more items per batch
Use the "Get More Recommendations" button to reveal additional batches beyond the first set

Archive buttons not working?

Archive buttons require READWISE_TOKEN in browser localStorage
Open browser console and run: localStorage.setItem('READWISE_TOKEN', 'your-token-here')
This is per-origin (GitHub Pages users need to set it once per browser)

Data fetch failing?

Verify READWISE_TOKEN is set: echo $READWISE_TOKEN
Check token is valid at https://readwise.io/access_token
Ensure internet connection is active

Scores seem low even for relevant articles?

Enable full-text scoring by running a full REST API fetch: fetch_later_incremental.js → merge_chunks.js → build_scored_cache.js
Without full text, scoring uses only the article title, summary, and author — body content is ignored
Alternatively, switch to the qmd engine ("engine": "qmd") which indexes full article text via BM25

QMD export seems to be missing documents?

Articles originally saved via Readwise's internal read.readwise.io URLs are not included in the export ZIP
Run a full REST API fetch first (fetch_later_incremental.js + merge_chunks.js) — the export script will backfill missing articles from the resulting fulltext JSON

Discovery search not working?

Discovery search requires the Readwise CLI: npm install -g @readwise/cli
Authenticate with readwise login-with-token your-token
Check that discovery.enabled is true in utils/settings.json

Project Structure

Readwise-recommender/
├── data/                              # Your data (gitignored)
│   ├── highlights_raw.json           # Cached highlights (optional, for profile generation)
│   ├── interest_profile.json         # Your thematic clusters
│   ├── recommendation_log.json       # Tracking previously recommended items
│   ├── documents_later.json          # Scored candidates from "Later" queue
│   ├── documents_later_fulltext.json # Extracted plain text map (from merge step)
│   ├── documents_<tag>.json          # Scored candidates from tagged collection
│   ├── qmd-docs/                     # Markdown export for qmd engine (Later queue)
│   └── qmd-tag-docs/                 # Markdown export for qmd engine (tagged docs)
│
├── output/
│   └── recommendations/               # Daily HTML files (local archive)
│       └── YYYY-MM-DD.html
│
├── utils/
│   ├── fetch_later_incremental.js    # Fetch "Later" docs via REST API (with full text)
│   ├── fetch_later_cli.js            # Fetch "Later" docs via CLI (metadata only, no full text)
│   ├── fetch_tag_incremental.js      # Fetch tagged documents
│   ├── merge_chunks.js               # Merge fetched data, extract full text
│   ├── build_scored_cache.js         # Score documents (keyword or qmd engine)
│   ├── scoring_engine.js             # Keyword scoring algorithm
│   ├── qmd_scoring_engine.js         # QMD BM25+vector scoring engine
│   ├── scoring_engine_selector.js    # Routes to keyword or qmd engine per settings
│   ├── export_docs_to_qmd.js         # Export Later docs as markdown for qmd
│   ├── export_tag_to_qmd.js          # Export tagged docs as markdown for qmd
│   ├── score_tag_documents.js        # Score tagged documents
│   ├── update_cache_from_yesterday.js # Incremental daily cache update
│   ├── discover_by_search.js         # Content-based discovery search (requires CLI)
│   ├── upload_recommendations.js     # SCP upload helper
│   ├── upload_github_pages.js        # GitHub Pages upload helper
│   ├── settings.example.json         # Configuration template
│   ├── settings.json                 # Your settings (gitignored)
│   ├── config.json.template          # SCP config template
│   └── config.json                   # SCP credentials (gitignored)
│
├── daily_recs.js                      # Main daily script
└── README.md                          # This file

Future Enhancements

Feedback Loop: Track read vs. skipped items and adjust theme weights automatically
Diversity Scoring: Ensure recommendations span multiple themes by default
Engagement Analysis: "What themes did I engage with most this month?"
Conversational Refinement: Natural language profile updates ("I want fewer philosophy, more practical advice")
Weekly Summaries: Reading pattern analysis and theme trends

Questions or feedback? Open an issue at https://github.com/yourusername/Readwise-recommender/issues

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
tests		tests
utils		utils
.gitignore		.gitignore
CONFIGURATION.md		CONFIGURATION.md
Claude.pdf		Claude.pdf
README.md		README.md
daily_recs.js		daily_recs.js

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Readwise Recommendation Engine

Overview

Prerequisites

Setup

1. Clone and Install

2. Set Your Readwise API Token

3. (Optional) Install the Readwise CLI

4. Create Your Interest Profile

Option A: Use Claude Code (Recommended)

Option B: Create Manually

5. Configure Settings

6. Fetch Initial Data

7. Generate Your First Recommendations

Daily Usage

Refreshing Cached Data

Configuration Reference

Hosting Options

Option 1: Local File (Easiest)

Option 2: GitHub Pages (Free Bookmarkable URL)

Option 3: Personal Web Server (SCP)

Option 4: No Upload (Local Archive Only)

How Scoring Works

Hybrid Scoring Algorithm

Scoring Engines

Keyword Engine (default)

QMD Engine

QMD Engine Setup

Prerequisites

Initial Export

Daily Sync

Known Limitation

Automating with Cron

Setup Instructions

Common Issues

Alternative: Using launchd (macOS)

Data Schemas

Interest Profile (data/interest_profile.json)

Recommendation Log (data/recommendation_log.json)

Troubleshooting

Project Structure

Future Enhancements

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Interest Profile (`data/interest_profile.json`)

Recommendation Log (`data/recommendation_log.json`)

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