Build a bookmark suggestion engine

Here’s a basic implementation of a Bookmark Suggestion Engine. This engine can suggest bookmarks based on user preferences, tags, and browsing history. It can be built as a Python module or web service.

1. Core Features

• Add bookmarks with tags & metadata

• Suggest bookmarks based on:

  • Keyword match

  • Most visited

  • Recently added

  • Tag similarity

  • User interest profile

2. Technologies Used

• Python

• SQLite or JSON for local storage

• Optional: Flask for web API

3. Python Code (Basic Version)

python
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import json
from collections import defaultdict
from difflib import get_close_matches

class BookmarkEngine:
    def __init__(self, db_path='bookmarks.json'):
        self.db_path = db_path
        self.bookmarks = self.load_data()

    def load_data(self):
        try:
            with open(self.db_path, 'r') as f:
                return json.load(f)
        except FileNotFoundError:
            return []

    def save_data(self):
        with open(self.db_path, 'w') as f:
            json.dump(self.bookmarks, f, indent=4)

    def add_bookmark(self, title, url, tags, visit_count=0):
        self.bookmarks.append({
            'title': title,
            'url': url,
            'tags': tags,
            'visit_count': visit_count
        })
        self.save_data()

    def suggest(self, query=None, top_n=5):
        if not self.bookmarks:
            return []

        if query:
            # Match by tag or title
            results = []
            for bm in self.bookmarks:
                if query.lower() in bm['title'].lower() or query.lower() in ' '.join(bm['tags']).lower():
                    results.append((bm, bm['visit_count']))
            results.sort(key=lambda x: x[1], reverse=True)
            return [bm for bm, _ in results[:top_n]]
        else:
            # Suggest based on visit count
            return sorted(self.bookmarks, key=lambda x: x['visit_count'], reverse=True)[:top_n]

    def update_visit(self, url):
        for bm in self.bookmarks:
            if bm['url'] == url:
                bm['visit_count'] += 1
                self.save_data()
                break

    def get_tags(self):
        tag_freq = defaultdict(int)
        for bm in self.bookmarks:
            for tag in bm['tags']:
                tag_freq[tag] += 1
        return sorted(tag_freq.items(), key=lambda x: x[1], reverse=True)

    def suggest_by_tag(self, tag, top_n=5):
        suggestions = [bm for bm in self.bookmarks if tag in bm['tags']]
        return sorted(suggestions, key=lambda x: x['visit_count'], reverse=True)[:top_n]

4. Usage Example

python
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engine = BookmarkEngine()

engine.add_bookmark("Python Docs", "https://docs.python.org/3/", ["python", "documentation"])
engine.add_bookmark("GitHub", "https://github.com", ["code", "repository"])
engine.add_bookmark("Stack Overflow", "https://stackoverflow.com", ["questions", "coding", "help"], visit_count=10)

print("Top Bookmarks:", engine.suggest())
print("Suggested for 'python':", engine.suggest("python"))
print("Popular Tags:", engine.get_tags())

5. Optional Web API with Flask

python
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from flask import Flask, request, jsonify

app = Flask(__name__)
engine = BookmarkEngine()

@app.route('/add', methods=['POST'])
def add():
    data = request.json
    engine.add_bookmark(data['title'], data['url'], data['tags'])
    return jsonify({"status": "added"})

@app.route('/suggest', methods=['GET'])
def suggest():
    query = request.args.get('q', '')
    results = engine.suggest(query)
    return jsonify(results)

@app.route('/tags', methods=['GET'])
def tags():
    return jsonify(engine.get_tags())

if __name__ == '__main__':
    app.run(debug=True)

6. Future Enhancements

• Use NLP to enhance tag similarity

• Store data in a proper database (e.g., SQLite, PostgreSQL)

• Track timestamps to improve recency-based suggestions

• Add user login and multi-user support

• Create a browser extension or UI

Let me know if you want a browser plugin version, user authentication, or a front-end interface.