20 Content-Based Algorithm Projects with PHP/MySQL Backend

awiskar acharya

February 20, 2026

Table of Contents

Introduction to Content-Based Algorithms

Content-based algorithms are recommendation systems that suggest items to users based on the features and attributes of items they've previously interacted with. Unlike collaborative filtering that relies on user behavior patterns, content-based approaches focus on item similarities using metadata, descriptions, and characteristics.

These algorithms work by:

Creating item profiles - Extracting features from items (keywords, categories, descriptions)
Building user profiles - Understanding user preferences based on their interactions
Calculating similarity - Matching user profiles with item features
Generating recommendations - Suggesting items with highest similarity scores

Key applications include movie recommendations, news articles, product suggestions, music playlists, and more.

20 Content-Based Algorithm Projects

Project 1: Movie Recommendation System

Description: Recommend movies based on genres, actors, and plot keywords that users have previously liked.

Features:

User rating input for movies
Movie database with genres, cast, director
Content similarity based on genre matching
"More like this" feature
Search functionality

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: TF-IDF with Cosine Similarity

Project 2: Recipe Finder Based on Ingredients

Description: Suggest recipes based on ingredients users have at home or prefer.

Features:

Input available ingredients
Recipe database with ingredients list
Match percentage calculation
Dietary preference filters (vegetarian, vegan, gluten-free)
Step-by-step cooking instructions

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Jaccard Similarity for ingredient matching

Project 3: News Article Recommender

Description: Recommend news articles based on reading history and topic preferences.

Features:

Category-based filtering (sports, politics, tech)
Keyword extraction from articles
Reading history tracking
Personalized news feed
Save/bookmark articles

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Keyword Frequency Analysis

Project 4: Book Recommendation Engine

Description: Suggest books based on genres, authors, and book descriptions.

Features:

Book database with genres and descriptions
Author similarity matching
Rating system
"Readers also liked" section
Wishlist functionality

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Content-based filtering using book metadata

Project 5: Music Playlist Generator

Description: Create personalized playlists based on genre, artist, and mood preferences.

Features:

Genre selection interface
Artist similarity matching
Mood-based filtering (happy, sad, energetic)
Playlist creation and saving
Song preview integration

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Feature-based similarity matching

Project 6: Job Recommendation System

Description: Match job seekers with relevant positions based on skills and experience.

Features:

User profile with skills and experience
Job database with requirements
Match percentage display
Application tracking
Save jobs for later

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Skill matching with weighted scoring

Project 7: E-commerce Product Recommender

Description: Suggest products based on browsing history and product categories.

Features:

Product catalog with categories
View history tracking
"Customers who viewed this also viewed"
Related products section
Price range filtering

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Category-based similarity with price consideration

Project 8: Course Recommendation for Students

Description: Recommend online courses based on completed courses and interests.

Features:

Course database with subjects and levels
Skill gap analysis
Learning path suggestions
Progress tracking
Course difficulty filtering

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Hierarchical content matching

Project 9: Travel Destination Recommender

Description: Suggest travel destinations based on preferences like climate, activities, budget.

Features:

Destination database with attributes
Preference questionnaire
Budget calculator
Seasonal recommendations
Itinerary planning

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Multi-attribute decision making

Project 10: Fitness Workout Planner

Description: Recommend workouts based on fitness goals, available equipment, and experience level.

Features:

Goal selection (weight loss, muscle gain)
Equipment availability input
Difficulty levels
Workout duration preferences
Progress tracking

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Rule-based content filtering

Project 11: Restaurant Finder with Menu Analysis

Description: Suggest restaurants based on cuisine preferences and dietary restrictions.

Features:

Restaurant database with cuisines
Menu item analysis
Price range filtering
Distance consideration
User reviews integration

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Cuisine similarity matching

Project 12: Academic Paper Recommender

Description: Suggest research papers based on keywords, authors, and citations.

Features:

Paper database with abstracts
Author tracking
Citation network
Keyword search
Save to reading list

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: TF-IDF with abstract analysis

Project 13: Podcast Episode Suggester

Description: Recommend podcast episodes based on topics, hosts, and listening history.

Features:

Topic categorization
Host preferences
Episode duration filter
Rating system
Playlist creation

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Content similarity with temporal factors

Project 14: Fashion Outfit Recommender

Description: Suggest clothing combinations based on style preferences and occasion.

Features:

Style preference quiz
Occasion-based filtering (casual, formal)
Color coordination algorithm
Weather consideration
Save favorite outfits

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Attribute-based matching

Project 15: Skill Development Path Finder

Description: Recommend learning paths based on current skills and career goals.

Features:

Skill assessment quiz
Career goal selection
Learning resource database
Skill progression tracking
Certificate recommendations

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Prerequisite-based content filtering

Project 16: Wine Recommendation System

Description: Suggest wines based on taste preferences, food pairings, and price range.

Features:

Taste profile questionnaire
Food pairing suggestions
Price range filtering
Region preferences
Rating and reviews

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Multi-criteria decision analysis

Project 17: YouTube Video Suggester

Description: Recommend videos based on watch history and video metadata.

Features:

Category preferences
Channel following
Duration filter
Watch later list
Similar video suggestions

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Metadata similarity matching

Project 18: Mobile App Recommender

Description: Suggest mobile apps based on categories, ratings, and user needs.

Features:

Category browsing
Popular apps display
Similar apps suggestion
User review analysis
Download links

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Feature-based similarity

Project 19: Hobby and Interest Finder

Description: Recommend hobbies based on personality traits and available time/resources.

Features:

Personality questionnaire
Time availability input
Budget consideration
Skill level matching
Local group suggestions

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Rule-based expert system

Project 20: Personalized News Digest

Description: Create daily news digest based on user interests and reading behavior.

Features:

Topic selection interface
Source preferences
Reading time estimation
Email digest option
Click tracking for relevance

Tech Stack: HTML, CSS, JavaScript, PHP, MySQL

Algorithm: Relevance scoring with user feedback

Common Implementation Structure for All Projects

Database Schema Template

-- Example structure for Movie Recommendation System
CREATE DATABASE content_recommender;
-- Users table
CREATE TABLE users (
id INT PRIMARY KEY AUTO_INCREMENT,
username VARCHAR(50),
email VARCHAR(100),
created_at TIMESTAMP
);
-- Items table (movies, products, etc.)
CREATE TABLE items (
id INT PRIMARY KEY AUTO_INCREMENT,
title VARCHAR(200),
description TEXT,
category VARCHAR(50),
image_url VARCHAR(500),
created_at TIMESTAMP
);
-- Item attributes table
CREATE TABLE item_attributes (
id INT PRIMARY KEY AUTO_INCREMENT,
item_id INT,
attribute_name VARCHAR(50),
attribute_value VARCHAR(200),
FOREIGN KEY (item_id) REFERENCES items(id)
);
-- User preferences table
CREATE TABLE user_preferences (
id INT PRIMARY KEY AUTO_INCREMENT,
user_id INT,
item_id INT,
rating INT,
viewed_at TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (item_id) REFERENCES items(id)
);
-- Recommendations cache
CREATE TABLE recommendations (
id INT PRIMARY KEY AUTO_INCREMENT,
user_id INT,
item_id INT,
similarity_score DECIMAL(5,4),
created_at TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (item_id) REFERENCES items(id)
);

PHP Backend Structure Example

<?php
// config.php
class Database {
private $host = "localhost";
private $username = "root";
private $password = "";
private $database = "content_recommender";
public $connection;
public function __construct() {
$this->connection = new mysqli($this->host, $this->username, 
$this->password, $this->database);
if ($this->connection->connect_error) {
die("Connection failed: " . $this->connection->connect_error);
}
}
}
// similarity_calculator.php
class SimilarityCalculator {
// Jaccard Similarity for set-based matching
public function jaccardSimilarity($set1, $set2) {
$intersection = array_intersect($set1, $set2);
$union = array_unique(array_merge($set1, $set2));
return count($intersection) / count($union);
}
// Cosine Similarity for vector-based matching
public function cosineSimilarity($vector1, $vector2) {
$dotProduct = 0;
$magnitude1 = 0;
$magnitude2 = 0;
foreach($vector1 as $key => $value) {
$dotProduct += $value * ($vector2[$key] ?? 0);
$magnitude1 += pow($value, 2);
}
foreach($vector2 as $value) {
$magnitude2 += pow($value, 2);
}
$magnitude1 = sqrt($magnitude1);
$magnitude2 = sqrt($magnitude2);
if($magnitude1 * $magnitude2 == 0) return 0;
return $dotProduct / ($magnitude1 * $magnitude2);
}
}
// recommender.php
class ContentBasedRecommender {
private $db;
private $similarity;
public function __construct() {
$database = new Database();
$this->db = $database->connection;
$this->similarity = new SimilarityCalculator();
}
public function getRecommendations($userId, $limit = 10) {
// Get user's preferred items
$preferredItems = $this->getUserPreferredItems($userId);
if(empty($preferredItems)) {
return $this->getPopularItems($limit);
}
// Get all items
$allItems = $this->getAllItems();
$scores = [];
foreach($allItems as $item) {
$maxScore = 0;
foreach($preferredItems as $preferred) {
$similarity = $this->calculateItemSimilarity($item['id'], $preferred['id']);
$weightedScore = $similarity * $preferred['rating'];
$maxScore = max($maxScore, $weightedScore);
}
if($maxScore > 0) {
$scores[] = [
'item_id' => $item['id'],
'score' => $maxScore
];
}
}
// Sort by score descending
usort($scores, function($a, $b) {
return $b['score'] <=> $a['score'];
});
return array_slice($scores, 0, $limit);
}
private function calculateItemSimilarity($itemId1, $itemId2) {
// Get attributes for both items
$attributes1 = $this->getItemAttributes($itemId1);
$attributes2 = $this->getItemAttributes($itemId2);
// Calculate similarity based on attributes
return $this->similarity->jaccardSimilarity($attributes1, $attributes2);
}
private function getUserPreferredItems($userId) {
$query = "SELECT item_id, rating FROM user_preferences 
WHERE user_id = ? AND rating >= 4 
ORDER BY rating DESC LIMIT 20";
$stmt = $this->db->prepare($query);
$stmt->bind_param("i", $userId);
$stmt->execute();
$result = $stmt->get_result();
$items = [];
while($row = $result->fetch_assoc()) {
$items[] = $row;
}
return $items;
}
private function getItemAttributes($itemId) {
$query = "SELECT attribute_name, attribute_value 
FROM item_attributes WHERE item_id = ?";
$stmt = $this->db->prepare($query);
$stmt->bind_param("i", $itemId);
$stmt->execute();
$result = $stmt->get_result();
$attributes = [];
while($row = $result->fetch_assoc()) {
$attributes[] = $row['attribute_name'] . ':' . $row['attribute_value'];
}
return $attributes;
}
}

Frontend Template (HTML/CSS/JavaScript)

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Content-Based Recommender System</title>
<style>
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
min-height: 100vh;
padding: 20px;
}
.container {
max-width: 1200px;
margin: 0 auto;
background: white;
border-radius: 20px;
padding: 30px;
box-shadow: 0 20px 60px rgba(0,0,0,0.3);
}
h1 {
color: #333;
margin-bottom: 30px;
text-align: center;
font-size: 2.5em;
}
.preference-section {
background: #f8f9fa;
padding: 20px;
border-radius: 10px;
margin-bottom: 30px;
}
.items-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
gap: 20px;
margin-top: 20px;
}
.item-card {
background: white;
border: 1px solid #e0e0e0;
border-radius: 10px;
padding: 15px;
transition: transform 0.3s, box-shadow 0.3s;
}
.item-card:hover {
transform: translateY(-5px);
box-shadow: 0 10px 30px rgba(0,0,0,0.1);
}
.item-title {
font-size: 1.2em;
font-weight: bold;
margin-bottom: 10px;
color: #333;
}
.item-description {
color: #666;
margin-bottom: 15px;
font-size: 0.9em;
line-height: 1.4;
}
.item-category {
display: inline-block;
background: #667eea;
color: white;
padding: 5px 10px;
border-radius: 15px;
font-size: 0.8em;
margin-bottom: 10px;
}
.rating {
color: #ffc107;
margin: 10px 0;
}
.btn {
background: #667eea;
color: white;
border: none;
padding: 10px 20px;
border-radius: 5px;
cursor: pointer;
font-size: 1em;
transition: background 0.3s;
}
.btn:hover {
background: #5a67d8;
}
.btn-secondary {
background: #48bb78;
}
.btn-secondary:hover {
background: #38a169;
}
.loading {
text-align: center;
padding: 50px;
display: none;
}
.loading-spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #667eea;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
margin: 0 auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
.error-message {
background: #fef2f2;
color: #dc2626;
padding: 10px;
border-radius: 5px;
margin: 10px 0;
display: none;
}
.success-message {
background: #f0fdf4;
color: #16a34a;
padding: 10px;
border-radius: 5px;
margin: 10px 0;
display: none;
}
.filter-section {
display: flex;
gap: 10px;
margin-bottom: 20px;
flex-wrap: wrap;
}
.filter-input {
flex: 1;
padding: 10px;
border: 2px solid #e0e0e0;
border-radius: 5px;
font-size: 1em;
min-width: 200px;
}
.filter-input:focus {
outline: none;
border-color: #667eea;
}
@media (max-width: 768px) {
.container {
padding: 15px;
}
h1 {
font-size: 2em;
}
.items-grid {
grid-template-columns: 1fr;
}
}
</style>
</head>
<body>
<div class="container">
<h1>Content-Based Recommender System</h1>
<div class="preference-section">
<h2>Your Preferences</h2>
<p>Rate items you like to get personalized recommendations</p>
<div class="filter-section">
<input type="text" class="filter-input" id="searchInput" 
placeholder="Search items...">
<select class="filter-input" id="categoryFilter">
<option value="">All Categories</option>
</select>
</div>
</div>
<div id="itemsContainer" class="items-grid">
<!-- Items will be loaded here -->
</div>
<div class="loading" id="loading">
<div class="loading-spinner"></div>
<p>Loading recommendations...</p>
</div>
<div class="error-message" id="errorMessage"></div>
<div class="success-message" id="successMessage"></div>
<div style="text-align: center; margin-top: 30px;">
<button class="btn" onclick="getRecommendations()">
Get Personalized Recommendations
</button>
</div>
<h2 style="margin-top: 40px;">Recommended For You</h2>
<div id="recommendationsContainer" class="items-grid">
<!-- Recommendations will be loaded here -->
</div>
</div>
<script>
// State management
let currentUser = 1; // Demo user ID
let items = [];
let categories = new Set();
// Load items on page load
document.addEventListener('DOMContentLoaded', function() {
loadItems();
loadUserPreferences();
setupEventListeners();
});
function setupEventListeners() {
document.getElementById('searchInput').addEventListener('input', filterItems);
document.getElementById('categoryFilter').addEventListener('change', filterItems);
}
function loadItems() {
showLoading();
fetch('api/get_items.php')
.then(response => response.json())
.then(data => {
if(data.success) {
items = data.items;
extractCategories();
renderItems(items);
populateCategoryFilter();
} else {
showError('Failed to load items');
}
})
.catch(error => {
showError('Error loading items: ' + error.message);
})
.finally(() => {
hideLoading();
});
}
function extractCategories() {
categories.clear();
items.forEach(item => {
if(item.category) {
categories.add(item.category);
}
});
}
function populateCategoryFilter() {
const filter = document.getElementById('categoryFilter');
categories.forEach(category => {
const option = document.createElement('option');
option.value = category;
option.textContent = category;
filter.appendChild(option);
});
}
function filterItems() {
const searchTerm = document.getElementById('searchInput').value.toLowerCase();
const category = document.getElementById('categoryFilter').value;
const filtered = items.filter(item => {
const matchesSearch = item.title.toLowerCase().includes(searchTerm) ||
item.description.toLowerCase().includes(searchTerm);
const matchesCategory = !category || item.category === category;
return matchesSearch && matchesCategory;
});
renderItems(filtered);
}
function renderItems(itemsToRender) {
const container = document.getElementById('itemsContainer');
container.innerHTML = '';
if(itemsToRender.length === 0) {
container.innerHTML = '<p style="text-align: center; grid-column: 1/-1;">No items found</p>';
return;
}
itemsToRender.forEach(item => {
const card = createItemCard(item);
container.appendChild(card);
});
}
function createItemCard(item) {
const card = document.createElement('div');
card.className = 'item-card';
card.dataset.itemId = item.id;
card.innerHTML = `
<div class="item-category">${item.category || 'Uncategorized'}</div>
<div class="item-title">${item.title}</div>
<div class="item-description">${item.description}</div>
<div class="rating" id="rating-${item.id}">
${generateRatingStars(item.userRating || 0)}
</div>
<button class="btn btn-secondary" onclick="rateItem(${item.id}, 5)">
Like
</button>
`;
return card;
}
function generateRatingStars(rating) {
let stars = '';
for(let i = 1; i <= 5; i++) {
if(i <= rating) {
stars += '★';
} else {
stars += '☆';
}
}
return stars;
}
function rateItem(itemId, rating) {
showLoading();
const formData = new FormData();
formData.append('user_id', currentUser);
formData.append('item_id', itemId);
formData.append('rating', rating);
fetch('api/rate_item.php', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
if(data.success) {
showSuccess('Rating saved successfully');
updateItemRating(itemId, rating);
} else {
showError('Failed to save rating');
}
})
.catch(error => {
showError('Error saving rating: ' + error.message);
})
.finally(() => {
hideLoading();
});
}
function updateItemRating(itemId, rating) {
const ratingElement = document.getElementById(`rating-${itemId}`);
if(ratingElement) {
ratingElement.innerHTML = generateRatingStars(rating);
}
}
function getRecommendations() {
showLoading();
fetch(`api/get_recommendations.php?user_id=${currentUser}`)
.then(response => response.json())
.then(data => {
if(data.success) {
renderRecommendations(data.recommendations);
} else {
showError('Failed to get recommendations');
}
})
.catch(error => {
showError('Error getting recommendations: ' + error.message);
})
.finally(() => {
hideLoading();
});
}
function renderRecommendations(recommendations) {
const container = document.getElementById('recommendationsContainer');
container.innerHTML = '';
if(recommendations.length === 0) {
container.innerHTML = '<p style="text-align: center; grid-column: 1/-1;">No recommendations yet. Rate some items first!</p>';
return;
}
recommendations.forEach(item => {
const card = createItemCard(item);
container.appendChild(card);
});
}
function loadUserPreferences() {
fetch(`api/get_user_preferences.php?user_id=${currentUser}`)
.then(response => response.json())
.then(data => {
if(data.success && data.preferences) {
data.preferences.forEach(pref => {
updateItemRating(pref.item_id, pref.rating);
});
}
})
.catch(error => {
console.error('Error loading preferences:', error);
});
}
function showLoading() {
document.getElementById('loading').style.display = 'block';
}
function hideLoading() {
document.getElementById('loading').style.display = 'none';
}
function showError(message) {
const errorEl = document.getElementById('errorMessage');
errorEl.textContent = message;
errorEl.style.display = 'block';
setTimeout(() => {
errorEl.style.display = 'none';
}, 3000);
}
function showSuccess(message) {
const successEl = document.getElementById('successMessage');
successEl.textContent = message;
successEl.style.display = 'block';
setTimeout(() => {
successEl.style.display = 'none';
}, 3000);
}
</script>
</body>
</html>

API Endpoints Structure

<?php
// api/get_items.php
header('Content-Type: application/json');
require_once '../config.php';
$db = new Database();
$query = "SELECT * FROM items ORDER BY created_at DESC";
$result = $db->connection->query($query);
$items = [];
while($row = $result->fetch_assoc()) {
$items[] = $row;
}
echo json_encode(['success' => true, 'items' => $items]);
// api/rate_item.php
header('Content-Type: application/json');
require_once '../config.php';
$userId = $_POST['user_id'];
$itemId = $_POST['item_id'];
$rating = $_POST['rating'];
$db = new Database();
// Check if rating exists
$checkQuery = "SELECT id FROM user_preferences 
WHERE user_id = ? AND item_id = ?";
$stmt = $db->connection->prepare($checkQuery);
$stmt->bind_param("ii", $userId, $itemId);
$stmt->execute();
$result = $stmt->get_result();
if($result->num_rows > 0) {
// Update existing rating
$query = "UPDATE user_preferences SET rating = ? 
WHERE user_id = ? AND item_id = ?";
$stmt = $db->connection->prepare($query);
$stmt->bind_param("iii", $rating, $userId, $itemId);
} else {
// Insert new rating
$query = "INSERT INTO user_preferences (user_id, item_id, rating) 
VALUES (?, ?, ?)";
$stmt = $db->connection->prepare($query);
$stmt->bind_param("iii", $userId, $itemId, $rating);
}
if($stmt->execute()) {
echo json_encode(['success' => true]);
} else {
echo json_encode(['success' => false, 'error' => $db->connection->error]);
}
// api/get_recommendations.php
header('Content-Type: application/json');
require_once '../config.php';
require_once '../recommender.php';
$userId = $_GET['user_id'];
$recommender = new ContentBasedRecommender();
$recommendations = $recommender->getRecommendations($userId);
// Get full item details for recommendations
$items = [];
foreach($recommendations as $rec) {
$query = "SELECT * FROM items WHERE id = ?";
$stmt = $db->connection->prepare($query);
$stmt->bind_param("i", $rec['item_id']);
$stmt->execute();
$result = $stmt->get_result();
$item = $result->fetch_assoc();
$item['similarity_score'] = $rec['score'];
$items[] = $item;
}
echo json_encode(['success' => true, 'recommendations' => $items]);

Setup Instructions

Install XAMPP/WAMP/MAMP for local development
Create database named content_recommender
Import the schema provided above
Place project files in htdocs/www folder
Configure database connection in config.php
Add sample data to test the system
Access via browser at http://localhost/project-name

Sample Data Insertion

-- Sample items
INSERT INTO items (title, description, category) VALUES
('Inception', 'A mind-bending thriller about dreams', 'Movies'),
('The Matrix', 'A hacker discovers reality is a simulation', 'Movies'),
('Interstellar', 'Astronauts travel through a wormhole', 'Movies');
-- Sample attributes
INSERT INTO item_attributes (item_id, attribute_name, attribute_value) VALUES
(1, 'genre', 'Sci-Fi'),
(1, 'director', 'Christopher Nolan'),
(2, 'genre', 'Action'),
(2, 'director', 'Wachowski Sisters'),
(3, 'genre', 'Sci-Fi'),
(3, 'director', 'Christopher Nolan');

Key Features Across All Projects

User Registration/Login - Track user preferences
Item Rating System - Collect user feedback
Similarity Calculation - Core algorithm implementation
Recommendation Display - Show personalized results
Search and Filter - Browse items manually
Responsive Design - Mobile-friendly interface
Real-time Updates - AJAX-based interactions
Caching Mechanism - Optimize performance

Algorithm Variations for Different Projects

Project Type	Best Algorithm	Implementation Complexity
Text-based items	TF-IDF + Cosine Similarity	High
Category-based	Jaccard Similarity	Low
Mixed attributes	Weighted scoring	Medium
Hierarchical	Tree-based matching	Medium
Rule-based	Decision trees	High

This comprehensive guide provides everything needed to build 20 different content-based recommendation systems. Each project can be customized by modifying the item attributes, similarity calculations, and frontend presentation to match the specific domain requirements.

Category : SOURCE CODE

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