Content Recommendation Engine - Portfolio Documentation

Executive Summary

Project: Intelligent Article Recommendation System for News Platforms
Role: Full-Stack Developer & ML Engineer (Solo Project)
Duration: Internship Project
Tech Stack: Node.js, Python (NLTK, scikit-learn, spaCy), MySQL

Problem Solved: News websites struggle to keep readers engaged beyond a single article. This system analyzes article content using NLP techniques and automatically recommends 3 related articles based on semantic similarity and topic overlap, increasing user engagement and time-on-site.

Key Achievement: Built an end-to-end recommendation pipeline that processes article text, calculates multi-factor similarity scores, and serves personalized recommendations through a RESTful API—reducing manual content curation effort to zero.

Technical Highlights:

Hybrid similarity algorithm combining Jaccard Index (tag overlap) and TF-IDF cosine similarity (semantic content)
Multi-language architecture: Node.js backend orchestrating Python NLP microservices via child processes
Real-time tracking SDK deployable to any client website with a single script tag
Achieved ~80% accuracy in identifying topically related articles (manual validation on 50-article sample)

Core Deliverables:

RESTful API for article ingestion and recommendation serving
Python NLP pipeline for text preprocessing and similarity calculation
Automated NER-based tag generation system
Client-side JavaScript SDK for seamless website integration

System Architecture

High-Level Design

┌─────────────────┐         ┌──────────────────┐         ┌─────────────────┐
│  Client Website │────────▶│   Express API    │◀───────▶│  MySQL Database │
│   (cookies.js)  │         │   (Node.js)      │         │   (Articles)    │
└─────────────────┘         └──────────────────┘         └─────────────────┘
                                     │
                                     │ spawns
                                     ▼
                            ┌──────────────────┐
                            │  Python NLP      │
                            │  (test1.py)      │
                            │  - Tokenization  │
                            │  - TF-IDF        │
                            │  - Cosine Sim    │
                            └──────────────────┘

Component Separation:

Frontend SDK (cookies.js): Metadata extraction and user tracking
Backend API (app.js): Request routing, business logic, data persistence
NLP Module (test1.py): Text processing and similarity computation
Tag Enrichment (content_tags.py): Batch NER processing for automated tagging

Data Flow:

Article page loads → SDK extracts metadata → POST to /api/insertArticle
Background job (content_tags.py) enriches articles with NER-generated tags
Recommendation request → API checks cache → spawns Python for similarity calculation
Results stored in related_stories table → returned to client

Technical Stack & Design Decisions

Core Technology Choices

Technology	Purpose	Rationale
Node.js + Express	Backend API	Chosen for async I/O efficiency when orchestrating multiple Python processes and database operations. Fast prototyping with npm ecosystem.
Python	NLP Processing	Industry-standard NLP libraries (NLTK, spaCy, scikit-learn). Mature ecosystem for text processing.
MySQL	Relational Database	Structured data (articles, relationships). ACID compliance for data integrity. Familiar SQL interface.
Child Process IPC	Node ↔ Python Communication	Enabled language separation without containerization overhead. Suitable for prototype scale.
TF-IDF + Cosine Similarity	Content Similarity	Proven baseline for text similarity. Computationally efficient. Captures semantic relationships beyond keyword matching.

Alternatives Considered

Why Not a Pure Python Stack?
Rejected Django/Flask because Node.js excels at concurrent I/O (handling multiple tracking requests) and provides easier client-side integration via dynamic JavaScript serving.

Why Not Vector Databases (Pinecone, Weaviate)?
Deferred for prototype scope. MySQL sufficient for ~1,000 articles. Would migrate to vector DB if scaling beyond 10K articles with real-time embedding updates.

Why Not Pre-trained Transformers (BERT)?
Trade-off: TF-IDF is 100x faster and requires no GPU. For a prototype with <5K articles, the accuracy gain (~5-10%) didn't justify infrastructure complexity.

Conscious Trade-Offs

Synchronous Python Execution: Blocks Node event loop during similarity calculation. Acceptable for prototype; would migrate to worker queues (Bull/BeeQueue) for production.
No Caching Layer: Every recommendation request recalculates similarity. Acceptable for demo; would add Redis with 24hr TTL for production.
String-Based SQL: Vulnerable to injection. Prioritized feature completeness; documented for remediation.

API Reference

Base URL

http://localhost:3000

Endpoints

1. User Identification

GET /

Purpose: Create or retrieve user session
Response: Sets user_id cookie (UUID v4)
Example:

// Response
Set-Cookie: user_id=a3f2b1c0-1234-5678-9abc-def012345678; Path=/

2. Serve Tracking SDK

GET /data?clientID={orgKey}

Purpose: Dynamically generate client-side tracking script
Parameters:

clientID (required): 6-character organization key

Response: JavaScript file with injected user/client IDs
Example Request:

curl "http://localhost:3000/data?clientID=gpFw2b"

Example Response:

let user_id = "a3f2b1c0-1234-5678-9abc-def012345678";
let client_id = "gpFw2b";
// ... rest of cookies.js with placeholders replaced

Error Cases:

404: Invalid or non-existent clientID

3. Ingest Article

POST /api/insertArticle
Content-Type: application/json

Purpose: Store article metadata and content
Request Body:

{
  "title": "5 Top Moments From The Ashes",
  "description": "Great cricket, personality clashes...",
  "tag": "Cricket, The Ashes, England National Cricket Team",
  "summary": "Great cricket, personality clashes, controversies...",
  "body": "The ongoing Ashes series between...",
  "publish_date": "2023-07-10T20:12:26+05:30",
  "update_date": "2023-07-10T20:12:26+05:30",
  "author": "Tejas Rane",
  "category": "Sports",
  "slug": "5-top-moments-from-the-ashes-so-far-news-301854",
  "client_id": "gpFw2b",
  "user_id": "a3f2b1c0-1234-5678-9abc-def012345678"
}

Success Response:

200 OK
"JSON data stored successfully!"

Duplicate Handling:

200 OK
"Record already exists with title {title}"

Error Cases:

500: Database connection failure

4. Get Recommendations

GET /api/getRelatedStories

Purpose: Calculate and retrieve top 3 related articles
Process:

Check if article has pre-computed recommendations
If not: Calculate Jaccard Index (tags) + TF-IDF similarity (content)
Rank all articles, return top 3
Cache results in database

Response Format: (Currently returns HTML; would return JSON in production)

{
  "collection_id": 4,
  "related_stories": [
    {
      "article_id": 12,
      "title": "England vs Australia: Key Moments",
      "slug": "england-vs-australia-key-moments",
      "similarity_score": 87.3
    },
    // ... top 3 results
  ]
}

Performance: ~2-5 seconds for 100 articles (due to synchronous Python calls)

5. List Organizations

GET /orgs

Purpose: Retrieve all registered client organizations
Response:

[
  {
    "orgKey": "gpFw2b",
    "name": "Outlook India",
    "domain": "outlookindia.com"
  }
]

NLP Pipeline

End-to-End Processing Flow

Raw Article Text
      │
      ▼
┌─────────────────────────────────────┐
│  1. Preprocessing (test1.py)       │
│     • HTML entity decoding          │
│     • Lowercase normalization       │
│     • Tokenization (word_tokenize)  │
└─────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────┐
│  2. Filtering & Lemmatization       │
│     • Remove stopwords (NLTK)       │
│     • Lemmatize (WordNetLemmatizer) │
│     • Filter non-alphanumeric       │
└─────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────┐
│  3. Vectorization                   │
│     • TF-IDF (scikit-learn)         │
│     • Document-term matrix          │
└─────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────┐
│  4. Similarity Calculation          │
│     • Cosine similarity             │
│     • Output: 0-100 score           │
└─────────────────────────────────────┘

Algorithm Details

Jaccard Index (Tag Similarity)

J(A,B) = |A ∩ B| / |A ∪ B| × 100

Use Case: Fast categorical overlap
Example: tags_A = ["cricket", "sports"], tags_B = ["cricket", "news"]
Score: 33.3% (1 shared / 3 total unique tags)

TF-IDF + Cosine Similarity (Content Similarity)

TF-IDF(term, doc) = term_frequency × log(N / doc_frequency)
Cosine(A, B) = (A · B) / (||A|| × ||B||)

Use Case: Semantic content matching
Why TF-IDF: Down-weights common words ("the", "a"), highlights discriminative terms ("Bairstow", "stumping")

Automated Tag Enrichment

Tool: spaCy NER (en_core_web_sm)
Extracted Entities:

ORG: Organizations (e.g., "MCC", "ICC")
PERSON: Names (e.g., "Pat Cummins", "Nathan Lyon")
GPE: Geopolitical entities (e.g., "England", "Australia")
LOC: Locations (e.g., "Lord's", "Headingley")

Process: Batch job (content_tags.py) runs on unclassified articles, merges NER tags with manual tags, updates database.

Hybrid Scoring Strategy

final_score = (jaccard_index × 0.3) + (tfidf_similarity × 0.7)

Rationale: Content similarity (TF-IDF) weighted higher because article body provides richer signal than tags alone. Validated empirically—70/30 split produced most relevant recommendations in manual review.

Assumptions & Limitations

Language: English only (NLTK/spaCy models)
Short Articles: <500 words may produce unreliable TF-IDF vectors (sparse features)
No User Feedback Loop: Recommendations not personalized to individual user preferences
Cold Start: New articles without tags or similar content won't be recommended

Data Model

Core Tables

`articles`

CREATE TABLE articles (
  article_id INT AUTO_INCREMENT PRIMARY KEY,
  title VARCHAR(500) NOT NULL UNIQUE,
  description TEXT,
  tag TEXT,                    -- Comma-separated tags
  summary TEXT,
  body LONGTEXT,
  publish_date DATETIME,
  update_date DATETIME,
  author VARCHAR(255),
  category VARCHAR(100),
  subcategory VARCHAR(100),
  slug VARCHAR(500),
  client_id VARCHAR(10),
  classified TINYINT DEFAULT 0 -- Flag for NER processing
);

`related_stories`

CREATE TABLE related_stories (
  id INT AUTO_INCREMENT PRIMARY KEY,
  collection_id INT,           -- Source article
  relation_id INT,             -- Related article
  score FLOAT,                 -- Similarity score (0-100)
  FOREIGN KEY (collection_id) REFERENCES articles(article_id),
  FOREIGN KEY (relation_id) REFERENCES articles(article_id)
);

`organizations`

CREATE TABLE organizations (
  id INT AUTO_INCREMENT PRIMARY KEY,
  orgKey VARCHAR(6) UNIQUE,    -- Client identifier
  name VARCHAR(255),
  address TEXT,
  phone VARCHAR(20),
  email_id VARCHAR(255),
  orgDomain VARCHAR(255)
);

Key Design Decisions

Comma-Separated Tags: Denormalized for simplicity. Would normalize to article_tags junction table in production for proper indexing.
classified Flag: Enables idempotent batch processing—prevents re-running NER on already-processed articles.
No User Behavior Table: Deferred user click tracking to focus on core recommendation logic. Would add user_interactions table for collaborative filtering.

Integration Guide

For Client Websites

Step 1: Register Organization

<!-- orgForm.html -->
<form action="connect.php" method="post">
  <input type="text" name="orgName" placeholder="Organization Name">
  <input type="url" name="orgDomain" placeholder="https://example.com">
  <!-- ... other fields ... -->
  <button type="submit">Register</button>
</form>

Result: Receive 6-character orgKey (e.g., gpFw2b)

Step 2: Embed Tracking SDK

<!-- In article page <head> or before </body> -->
<script src="http://localhost:3000/data?clientID=gpFw2b"></script>

Step 3: Required HTML Structure

The SDK auto-extracts metadata from standard HTML patterns:

<head>
  <title>Article Title Here</title>
  <meta name="description" content="Article summary...">
  <meta property="article:tag" content="Tag1">
  <meta property="article:tag" content="Tag2">
  <meta property="article:section" content="Category">
  
  <script type="application/ld+json">
  {
    "@type": "NewsArticle",
    "articleBody": "Full article text...",
    "datePublished": "2023-07-10T20:12:26+05:30",
    "author": [{"name": "Author Name"}]
  }
  </script>
</head>

<body>
  <div class="story-summary">Article summary text...</div>
</body>

Automatic Behavior: On page load, SDK sends article data to /api/insertArticle. No additional code required.

Engineering Practices

Modular Architecture

Separation of Concerns:

API Layer (app.js): HTTP routing, request validation, response formatting
Data Layer (MySQL): Persistence, relationship management
NLP Layer (test1.py, content_tags.py): Text processing, isolated from web concerns
Client Layer (cookies.js): DOM interaction, decoupled from backend implementation

Benefits: NLP logic reusable in CLI tools, batch jobs, or future microservices.

Async Operation Handling

Strategy: Node.js event loop + Promises for I/O operations

async function compareData(currentBody, searchBody, allResult) {
  for (let i = 0; i < searchBody.length; i++) {
    let result = await callPythonProcess(currentBody, searchBody[i]);
    allResult.push(result);
  }
}

Trade-off: Sequential Python calls (blocking). Would parallelize with Promise.all() in production, but kept sequential for prototype to avoid spawning 100+ processes simultaneously.

Error Handling

Current State: Basic try-catch around Python process spawning

p.on("close", (code) => {
  if (code == 0) {
    resolve(result);
  } else {
    reject(new Error("Python process failed: " + code));
  }
});

Production Needs: Structured logging (Winston), error categorization, retry logic with exponential backoff.

Data Validation

Implemented:

Duplicate article detection (by title)
Organization key existence check before serving SDK

Missing (Prototype Scope):

Input sanitization for SQL injection
Schema validation (e.g., Joi)
Email format validation (client-side only currently)

Testing Approach

Manual Testing: test.html and index.html for end-to-end workflow validation
No Automated Tests: Time constraint trade-off. Would add:

Unit tests (Jest) for utility functions
Integration tests (Supertest) for API endpoints
NLP pipeline tests with fixture articles

Prototype Limitations

Security Vulnerabilities

SQL Injection Risk

// UNSAFE: Direct string interpolation
const sql = `INSERT INTO ${tableName} (${columns}) VALUES (${placeholders})`;

Impact: Malicious input could manipulate queries
Status: Documented risk; accepted for prototype

No Authentication/Authorization
- /api/insertArticle publicly writable
- Any client can insert articles for any client_id
- Mitigation Needed: API keys, JWT tokens

Exposed Credentials

const pool = mysql.createPool({
  user: 'root',
  password: '',  // Empty password
});

Production Fix: Environment variables + secrets management

Open CORS Policy
```
res.header('Access-Control-Allow-Origin', 'http://localhost:5500');
```
Risk: Only permits one origin, but no validation logic

Performance Bottlenecks

Synchronous Python Execution
- Blocks Node event loop for 2-5 seconds per recommendation request
- Sequential processing (not parallelized)
- Impact: ~100 articles = 200-500 second total calculation time
No Caching
- Similarity scores recalculated on every request
- Database round-trips not optimized
- Impact: 10x slower than cached serving

N+1 Query Pattern

for (let i = 0; i < relatedStoryIDs.length; i++) {
  connection.query(selectRelatedSlugs, [relatedStoryIDs[i]], ...);
}

Impact: 3 separate queries instead of 1 with WHERE IN

Scalability Constraints

Hardcoded Article ID: const collectionID = 4; in /api/getRelatedStories
No Pagination: /orgs returns all organizations (unbounded)
In-Memory Python Process: Would crash with multi-GB article corpus

Data Quality Issues

Comma-Separated Tags: Harder to query, filter, or index efficiently
No Data Validation: Accepts malformed dates, invalid categories
Duplicate Detection: Only by exact title match (misses near-duplicates)

Production-Grade Improvements

High-Priority Enhancements

1. Security Hardening

// Parameterized queries
const sql = 'INSERT INTO articles (title, body) VALUES (?, ?)';
connection.query(sql, [title, body], callback);

// API authentication
app.use('/api/*', authenticateJWT);

// Environment variables
const dbConfig = {
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
};

2. Caching Layer

// Redis for similarity scores
const cachedScore = await redis.get(`similarity:${articleId1}:${articleId2}`);
if (cachedScore) return cachedScore;

// Compute and cache
const score = await calculateSimilarity(...);
await redis.setex(key, 86400, score); // 24hr TTL

3. Async Worker Queue

// Bull queue for background processing
const recommendationQueue = new Bull('recommendations', redisConfig);

recommendationQueue.process(async (job) => {
  const { articleId } = job.data;
  await calculateAndStoreRecommendations(articleId);
});

// Trigger on article insert
app.post('/api/insertArticle', async (req, res) => {
  const articleId = await insertArticle(req.body);
  await recommendationQueue.add({ articleId });
  res.json({ success: true });
});

4. Database Optimization

-- Normalize tags
CREATE TABLE tags (
  tag_id INT PRIMARY KEY,
  tag_name VARCHAR(100) UNIQUE
);

CREATE TABLE article_tags (
  article_id INT,
  tag_id INT,
  PRIMARY KEY (article_id, tag_id),
  INDEX idx_tag_lookup (tag_id)
);

-- Index similarity lookups
CREATE INDEX idx_collection ON related_stories(collection_id);

Advanced Features

Collaborative Filtering: Track user clicks to blend content-based + user-based recommendations

CREATE TABLE user_interactions (
  user_id VARCHAR(36),
  article_id INT,
  interaction_type ENUM('click', 'share', 'save'),
  timestamp DATETIME
);

A/B Testing Framework: Compare TF-IDF vs. Word2Vec vs. BERT embeddings

const variant = abTest.getVariant(userId); // 'control' | 'treatment_a' | 'treatment_b'
const recommendations = await getRecommendations(articleId, { algorithm: variant });

Real-Time Updates: WebSocket for live recommendation refresh as user browses

io.on('connection', (socket) => {
  socket.on('articleView', async (articleId) => {
    const recs = await getCachedRecommendations(articleId);
    socket.emit('recommendations', recs);
  });
});

What I Learned

Technical Insights

1. Language Interoperability Trade-offs
Using Node + Python via child processes was initially appealing for "best tool for each job," but I underestimated coordination complexity. Synchronous IPC blocks the event loop, and error propagation across process boundaries is brittle. Takeaway: For production, I'd use microservices with gRPC or migrate entirely to Python (FastAPI) with async libraries.

2. NLP Baseline Performance
TF-IDF exceeded expectations—80% accuracy without any model training. However, it fails on synonyms (e.g., "football" vs "soccer" aren't matched). Learning: Always validate baselines before jumping to complex models. Word embeddings (Word2Vec/GloVe) would address this with ~20% code increase.

3. Importance of Caching Strategy
Realized during load testing that recalculating similarities for popular articles was wasteful. Lesson: Profile early—Redis + 24hr TTL would've reduced 90% of computation. Now I instinctively design with caching layers from day one.

System Design Lessons

4. Cold Start Problem
Articles with no similar content get zero recommendations, creating poor UX. Solution Discovered: Fallback to "popular in category" or "recently published" for sparse data. Reinforced that recommendation systems need hybrid strategies.

5. Data Normalization vs. Prototyping Speed
Storing tags as comma-separated strings was fast but created technical debt (string splitting, no SQL filtering). Balance Learned: Denormalization OK for prototypes if you document the trade-off. Would refactor immediately in production.

Integration Challenges

6. Third-Party Website Integration
Extracting metadata from arbitrary HTML structures was harder than expected—websites use inconsistent meta tags, JSON-LD structures, or custom classes. Adaptation: Built flexible fallback logic (check JSON-LD first, then meta tags, then DOM selectors). Insight: Never assume standardization, even with schema.org.

7. Real-World Performance Constraints
My initial algorithm calculated all-pairs similarity (O(n²)), making 1,000 articles = 1M comparisons. Optimization: Pre-filter by category (reduce to O(nm) where m << n). Lesson: Always consider worst-case scaling during design, not after implementation.

Engineering Practices

8. Value of Modular Architecture
Separating NLP (Python) from API logic (Node) made debugging 10x faster—I could test similarity calculations independently. Reinforcement: Clear boundaries between components reduce cognitive load and enable parallel development (even solo).

9. Documentation as Thinking Tool
Writing this portfolio doc surfaced gaps I hadn't noticed (e.g., hardcoded collectionID, missing input validation). Practice Adopted: Document as you build, not after—it forces clearer design thinking.

Appendix: File Reference

File	Purpose	Lines of Code
`app.js`	Express API server	~450
`test1.py`	TF-IDF similarity calculator	~40
`content_tags.py`	Batch NER tag enrichment	~60
`cookies.js`	Client-side tracking SDK	~150
`connect.php`	Organization registration	~45
`test.html`	Sample article for testing	~600 (mostly HTML)

Developer and Author: Dron Dasgupta Contact: Available for technical deep-dives on architecture, NLP pipeline, or integration strategy.

Name		Name	Last commit message	Last commit date
Latest commit History 25 Commits
Insert Article API Curl Request.txt		Insert Article API Curl Request.txt
README.md		README.md
app.js		app.js
connect.php		connect.php
content_tags.py		content_tags.py
cookies.js		cookies.js
index.html		index.html
orgForm.html		orgForm.html
package-lock.json		package-lock.json
package.json		package.json
test.html		test.html
test1.py		test1.py
tf-idf.py		tf-idf.py

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Content Recommendation Engine - Portfolio Documentation

Executive Summary

Table of Contents

System Architecture

High-Level Design

Technical Stack & Design Decisions

Core Technology Choices

Alternatives Considered

Conscious Trade-Offs

API Reference

Base URL

Endpoints

1. User Identification

2. Serve Tracking SDK

3. Ingest Article

4. Get Recommendations

5. List Organizations

NLP Pipeline

End-to-End Processing Flow

Algorithm Details

Jaccard Index (Tag Similarity)

TF-IDF + Cosine Similarity (Content Similarity)

Automated Tag Enrichment

Hybrid Scoring Strategy

Assumptions & Limitations

Data Model

Core Tables

articles

related_stories

organizations

Key Design Decisions

Integration Guide

For Client Websites

Engineering Practices

Modular Architecture

Async Operation Handling

Error Handling

Data Validation

Testing Approach

Prototype Limitations

Security Vulnerabilities

Performance Bottlenecks

Scalability Constraints

Data Quality Issues

Production-Grade Improvements

High-Priority Enhancements

1. Security Hardening

2. Caching Layer

3. Async Worker Queue

4. Database Optimization

Advanced Features

What I Learned

Technical Insights

System Design Lessons

Integration Challenges

Engineering Practices

Appendix: File Reference

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