Last Updated: September 2025
This document provides detailed guidance on complying with third-party platform Terms of Service when using BookScrapeDB_Recommends.
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- Goodreads: Book metadata, user reviews, ratings, reviewer data, user profiles and followings, feeds, tags, deep book data/settings, reading lists
- Google Books: Google user reviews/insights, Bibliographic data, book metadata, cover images, links, external ref to flexible authoritative or open-source user generated
- OpenLibrary: Catalog data, subject classifications, community contributions, tags, external user generated data opensource links/platforms/community
- ISBNdb: supply chain, market pricing, editions, validation authoritative IDs and publisher/licensor, series editions, literature market data
- External sources: Independently published catalogs, attributions, indirect user contet from indepedent sites, and references linked from the above platforms
- Google Books API: Official access with API key and rate limiting
- OpenLibrary API: Open data initiative (public domain)
- ISBNdb API: Subscription-based access for ISBN validation
- Reviewer identities: Processed but anonymized in outputs
- User reviews: Transformed into sentiment scores, embeddings, and statistical models
- Profile data: Aggregated for behavioral analysis, not individually identifiable
- No authentication credentials or private data accessed
- Large Language Models (LLMs): Used for review sentiment analysis and reader profiling
- Retrieval-Augmented Generation (RAG): Hybrid Vector+Graph embeddings for recommendations
- Natural Language Processing (NLP): Text mining on publicly available review content
- Graph Analytics: Network analysis on reading communities and influence patterns
This guide is for informational purposes only and does NOT constitute legal advice.
Users of this code are solely responsible for:
- Determining applicable laws in their jurisdiction
- Obtaining necessary permissions for their specific use case
- Ensuring compliance with platform Terms of Service
- Consulting legal counsel for production or commercial deployments
Official ToS: Goodreads Terms of Use
Last Updated: April 28, 2021
Owner: Goodreads LLC (Amazon)
Goodreads ToS Section 1 explicitly prohibits a lot of usage of scraping/mining
What this means:
- Automated scraping tools are explicitly prohibited without authorization
- Collection of reviews, book data, or user profiles requires permission
- Publicly visible data still subject to Terms of Service
- Commercial use requires explicit written agreements
For Production/Commercial Use: Any production deployment or commercial use of data collection from Goodreads was/will be conducted through:
- ✅ Explicit written agreements obtained from Goodreads prior to data collection
- ✅ Dated Terms of Service compliance verified at time of data collection
- ✅ Regulatory compliance with applicable data protection laws
- ✅ Small-scale compliant collection with express permission when required
For Educational/Research/Portfolio Use: Outside of production contexts, this codebase serves as:
- 📚 Demonstration of ETL pipeline architecture and data engineering concepts
- 🎓 Educational resource for learning graph analytics and recommendation systems
- 🔬 Research tool for academic exploration of literary network analysis
- 💼 Portfolio showcase of technical capabilities in data science
Respectful Practices Implemented:
- ✅ robots.txt compliance: Respects crawling directives
- ✅ Rate limiting: Prevents server overload (2+ second delays)
- ✅ Transparent User-Agent: Identifies crawler honestly
- ✅ Incremental processing: Minimizes redundant requests
- ✅ No bulk redistribution: Data transformed into aggregated insights
Important
User-generated content in this project is NOT exclusively from Goodreads.
This platform aggregates data from multiple sources:
Primary User Review Sources:
- Google Books: Google user reviews and ratings (separate from Goodreads)
- OpenLibrary: Community-contributed reviews and annotations
- External Catalogs: Independent literary websites and databases linked from the above platforms
- Public Domain Sources: Open-source literary databases and review aggregators
Cross-Referenced Content:
- Goodreads, Google Books, and OpenLibrary frequently link to external sources
- These external platforms host user-generated content under their own Terms of Service
- Many external sites explicitly allow scraping or provide open APIs
- Cross-referencing ISBN data connects reviews across multiple platforms
Example Data Flow:
ISBN 978-0-123456-78-9 →
├── Goodreads: Book metadata + review counts
├── Google Books: API data + Google user reviews
├── OpenLibrary: Catalog data + community annotations
└── External sites: Independent reviews from linked sources
Result: User review data originates from diverse sources, not solely Goodreads. This multi-source approach:
- Reduces dependency on any single platform
- Respects individual platform limitations
- Enriches data quality through cross-validation
- Distributes collection across compliant channels
| Use Case | Compliance Status | Notes |
|---|---|---|
| Educational demonstration | Acceptable | Non-commercial, transformative use |
| Academic research | Acceptable with attribution | Small-scale, proper citation |
| Portfolio showcase | Acceptable | Technical capability demonstration |
| Production deployment | Requires authorization | Must obtain explicit permission |
| Commercial service | Requires licensing | Written agreements mandatory |
For Educational/Research Users:
- Acknowledge limitations in documentation and presentations
- Use synthetic data for public demonstrations when possible
- Keep scale small - collect only what's necessary for learning
- Cite properly - attribute data sources in academic work
- Respect robots.txt - always check and comply
For Production/Commercial Users:
- Contact Goodreads Legal: copyright@goodreads.com
- Document your use case with specific data needs
- Propose compliance measures (rate limits, attribution, etc.)
- Obtain written permission before deployment
- Review terms periodically - Terms of Service may change
Alternative: Official API (When Available)
- Monitor for API reinstatement announcements
- Register for API access when/if it becomes available
- Transition from scraping to official API endpoints
Official API Terms: Google Books API Terms
Owner: Google LLC
✅ Fully compliant when using official Google Books API with proper authorization.
Requirements:
- Valid API key (register at Google Cloud Console)
- Rate limiting: Default 1,000 queries/day (adjustable with quotas)
- Proper attribution: "Powered by Google Books" or similar
- No bulk downloading of full book content (metadata only)
API Usage:
# Example compliant implementation
import requests
API_KEY = os.environ.get('GOOGLE_BOOKS_API_KEY')
RATE_LIMIT_DELAY = 0.1 # 100ms between requests
def fetch_google_books_data(isbn):
url = f"https://www.googleapis.com/books/v1/volumes?q=isbn:{isbn}&key={API_KEY}"
time.sleep(RATE_LIMIT_DELAY)
response = requests.get(url)
return response.json()Data Collected:
- ✅ Book metadata (titles, authors, publishers, ISBNs)
- ✅ Descriptions and subject classifications
- ✅ Cover image URLs
- ✅ Retail/list pricing information
- ✅ Google user reviews (separate from Goodreads reviews)
Google Reviews vs. Goodreads Reviews:
- Google Books has its own user review system
- These reviews are separate from and independent of Goodreads
- Accessible via official API with proper authentication
- Subject to Google's Terms of Service, not Goodreads'
Rate Limiting:
# Recommended delays
GOOGLE_BOOKS_DELAY = 0.1 # 100ms (well within rate limits)Caching:
# Cache API responses to minimize redundant calls
cache_file = 'google_books_cache.json'
# Reduces API usage by 95%+ on subsequent runsAttribution:
- Include "Data from Google Books" in outputs
- Link back to Google Books when displaying book data
- Respect thumbnail usage guidelines
Official ToS: OpenLibrary Terms
Owner: Internet Archive (Non-profit)
Data License: Open data initiative - public domain where applicable
✅ Fully compliant - OpenLibrary explicitly supports open data access.
Key Advantages:
- No API key required for basic access
- Generous rate limits for non-commercial use
- Encourages data reuse for educational/research purposes
- Provides bulk data dumps for large-scale projects
API Access:
# OpenLibrary API - No authentication required
url = f"https://openlibrary.org/api/books?bibkeys=ISBN:{isbn}&format=json&jscmd=data"
response = requests.get(url)Data Collected:
- ✅ Catalog metadata (titles, authors, editions)
- ✅ Subject classifications and Library of Congress data
- ✅ Community-contributed content
- ✅ Links to external sources (other libraries, databases)
- ✅ Community reviews and annotations (separate user base from Goodreads)
External Sources via OpenLibrary:
- OpenLibrary links to hundreds of external catalogs and databases
- These linked sources often have their own user-generated content
- Cross-referencing ISBNs discovers reviews on independent platforms
- Each external source subject to its own Terms of Service
Rate Limiting:
OPENLIBRARY_DELAY = 1.0 # 1 second (respectful, not required)Attribution:
- Credit Internet Archive/OpenLibrary in documentation
- Link back to OpenLibrary records when appropriate
- Consider donating to support the non-profit mission
Bulk Data Access:
- For large projects, use official data dumps: https://openlibrary.org/developers/dumps
- More efficient and respectful than scraping
Official ToS: ISBNdb Terms
Owner: ISBNdb.com
✅ Requires API subscription for access.
Access Tiers:
- Free Tier: 100 requests/day (educational use)
- Paid Tiers: Higher limits + commercial rights
- Enterprise: Bulk access with custom agreements
API Usage:
API_KEY = os.environ.get('ISBNDB_API_KEY')
headers = {'Authorization': API_KEY}
url = f"https://api2.isbndb.com/book/{isbn}"
response = requests.get(url, headers=headers)Data Collected:
- ✅ ISBN validation and edition tracking
- ✅ Pricing statistics (retail/list prices)
- ✅ Publisher information
- ✅ Format and availability data
Subscription Management:
- Monitor daily quota usage
- Implement caching to minimize API calls
- Upgrade tier if approaching limits
Commercial Use:
- Free tier: Educational/research only
- Commercial deployment: Requires paid subscription
- Review terms for redistribution rights
Important: A significant portion of user-generated content in this project comes from external sources linked by primary platforms.
How External Sources Are Discovered:
- ISBN Cross-Referencing: Same ISBN appears across multiple platforms
- Platform Links: Goodreads/Google Books/OpenLibrary link to external catalogs
- Independent Databases: Literary websites, review aggregators, library systems
- Open-Source Platforms: Community-driven book databases with permissive licenses
Academic & Library Systems:
- WorldCat (OCLC)
- Library of Congress
- University library catalogs
- Digital library consortia
Independent Review Platforms:
- LibraryThing
- BookLikes
- Litsy
- Shelfari (archived)
Public Domain & Open Data:
- Project Gutenberg
- HathiTrust
- Archive.org literary collections
- Wikipedia book articles
Commercial Aggregators:
- Amazon (reviews via API)
- Barnes & Noble
- Kobo
- Indie bookseller sites
For Each External Source:
- Verify Terms of Service: Check robots.txt and ToS before scraping
- Respect Rate Limits: Implement appropriate delays
- Provide Attribution: Credit original sources in documentation
- Use APIs When Available: Prefer official APIs over scraping
- Document Permissions: Maintain records of ToS compliance
Example Compliance Check:
# Before scraping any external source
def check_compliance(domain):
# 1. Check robots.txt
robots_url = f"https://{domain}/robots.txt"
# 2. Verify ToS allows data collection
tos_url = f"https://{domain}/terms"
# 3. Implement appropriate rate limiting
DELAY = 2.0 # Conservative default
# 4. Use transparent User-Agent
USER_AGENT = "BookScrapeDB/1.0 Educational Research"Key Point: By aggregating user reviews from multiple independent sources, this project:
✅ Reduces reliance on any single platform
✅ Respects individual platform limits by distributing collection
✅ Enriches data quality through cross-validation
✅ Mitigates compliance risk by diversifying sources
✅ Provides attribution to original content creators
Data Provenance Tracking:
# Each review tagged with source platform
review = {
'text': 'Review content...',
'rating': 4.5,
'source': 'google_books', # or 'openlibrary', 'external_catalog', etc.
'source_url': 'https://...',
'collected_date': '2025-01-15'
}Recommended Delays Between Requests:
# Platform-specific delays (in seconds)
RATE_LIMITS = {
'goodreads': 2.0, # Conservative (educational use only)
'google_books': 0.1, # Within API rate limits
'openlibrary': 1.0, # Respectful, not strictly required
'isbndb': 0.5, # API subscription allows faster
'external_default': 2.0 # Conservative for unknown sources
}Always check before scraping:
from urllib.robotparser import RobotFileParser
def can_fetch(url):
rp = RobotFileParser()
rp.set_url(f"{url.split('/')[0]}//robots.txt")
rp.read()
return rp.can_fetch("*", url)Common robots.txt Examples:
- Goodreads: Restricts most crawlers (respect these restrictions)
- Google Books: Allows API access, restricts bulk scraping
- OpenLibrary: Generally permissive for non-commercial use
Use transparent, identifiable User-Agent strings:
USER_AGENT = (
"BookScrapeDB/1.0 "
"(Educational Research Project; "
"+https://github.com/ChiefsBestPal/BooksScrapeDatabase_and_Recommender; "
"contact@example.com)"
)Why this matters:
- ✅ Allows platforms to contact you if issues arise
- ✅ Demonstrates good faith and transparency
- ✅ Distinguishes your crawler from malicious bots
- ✅ Enables platform-specific rate limiting
Protect user privacy in all outputs:
# Anonymize reviewer identities
reviewer_hash = hashlib.sha256(username.encode()).hexdigest()[:12]
# Never export mappings between usernames and IDs
# Store only aggregated statisticsAlways credit data sources:
## Data Sources
This project uses data from:
- Goodreads™ (deep book metadata/details, author/community/reviewers content and profiles, lists/feeds)
- Google Books™ (google users, API data and user reviews)
- OpenLibrary™ (catalog data and community content)
- ISBNdb™ (ISBN validation, market reltated data)
- [List external sources used]
All trademarks are property of their respective owners.Legal Requirements:
- Review all applicable platform Terms of Service
- Obtain explicit written permissions where required
- Document compliance measures in writing
- Consult legal counsel for your specific jurisdiction
- Verify GDPR/CCPA compliance if applicable
Technical Requirements:
- Implement robust rate limiting
- Set up monitoring and alerting for API issues
- Configure secure credential storage (not in code!)
- Implement data anonymization in all outputs
- Set up logging for compliance auditing
Documentation Requirements:
- Create internal compliance documentation
- Document data sources and permissions obtained
- Maintain records of Terms of Service versions at time of collection
- Establish data retention and deletion policies
- Prepare user-facing privacy policy
Regular Reviews:
- Review platform Terms of Service quarterly for changes
- Monitor for API deprecation announcements
- Update rate limits if platform policies change
- Audit data collection practices annually
Incident Response:
- Prepare process for cease-and-desist responses
- Document escalation procedures
- Maintain legal counsel contact information
- Have data deletion procedures ready
When available, always prefer official APIs:
- ✅ Google Books API: Fully supported and compliant
- ✅ OpenLibrary API: Open data, no restrictions
- ✅ ISBNdb API: Commercial options available
⚠️ Goodreads API: Deprecated (2021), monitor for reinstatement, alternative platforms and/or obtain explicite agreement/permission with copyrights teams at Goodreads or Amazon LLCs
For commercial projects, consider:
- Direct partnerships with platforms
- Licensed data access agreements
- Data sharing arrangements
- Revenue-sharing models
For demonstrations and testing:
- Generate synthetic book/review data
- Use public domain literature (Project Gutenberg)
- Create representative sample datasets
- Anonymize and aggregate real data
Alternative approach:
- Build opt-in data contribution system
- Allow users to import their own reading data
- Goodreads provides data export tools (users can share voluntarily)
- This approach is fully compliant with all ToS
Many jurisdictions provide research exemptions, but these are limited:
What's typically allowed:
- Small-scale collection for academic research
- Proper citation and attribution
- Non-commercial publication of findings
- Transformative use (analysis, not redistribution)
What's still restricted:
- Large-scale commercial use
- Bulk redistribution of data
- Bypassing technical protection measures
- Violating explicit Terms of Service
Important: Academic exemptions vary by jurisdiction and do not override contractual ToS agreements in all cases.
For academic projects:
- Consult your institution's IRB (Institutional Review Board)
- Review data ethics policies
- Obtain necessary approvals for human subjects research
- Document compliance with institutional guidelines
When publishing research using this data:
- ✅ Cite all data sources properly
- ✅ Describe data collection methodology
- ✅ Acknowledge any Terms of Service considerations
- ✅ Share aggregated findings, not raw data
- ✅ Make anonymized datasets available when possible (if permitted)
Stay informed about:
- Terms of Service updates (check quarterly)
- API deprecations or new releases
- Rate limit changes
- Enforcement policy updates
Resources:
- Platform developer blogs
- API documentation change logs
- Legal/compliance newsletters
- GitHub security advisories
This compliance guide is updated as:
- Platform terms change
- New data sources are added
- Compliance best practices evolve
- User feedback identifies gaps
Check the "Last Updated" date regularly.
For permissions or clarifications:
- Goodreads: copyright@goodreads.com
- Google Books: See Google Books Partner Program
- OpenLibrary: See OpenLibrary Contact
- ISBNdb: See ISBNdb Support
For questions about this compliance guide:
- Open a GitHub issue
- Contact @ChiefsBestPal via GitHub
- See
SECURITY.mdfor security concerns
IMPORTANT: This document does NOT constitute legal advice.
The information provided is for educational purposes and represents the maintainer's understanding of compliance requirements as of the last update date.
You are responsible for:
- ✅ Conducting your own legal analysis
- ✅ Consulting qualified legal counsel
- ✅ Ensuring compliance in your specific jurisdiction
- ✅ Obtaining necessary permissions for your use case
- ✅ Monitoring for changes in applicable laws and terms
The author makes NO warranties about the accuracy, completeness, or legal sufficiency of this guidance.
By using this software, you acknowledge that you have read this compliance guide and accept sole responsibility for ensuring your use complies with all applicable laws and Terms of Service.
Copyright © 2024-2025 Antoine Cantin