Game recomendation engine: admin panel, db seeding, user context

Project #8 of 10 in "Backend Development for serious guys!" progression
Python
Game recomendation engine: admin panel, db seeding, user context thumbnail

Updated: Oct 14, 2026

CodePen

See the Pen Game recomendation engine: admin panel, db seeding, user context by _Dave (@_Dave) on CodePen.

Learning plan

  1. Admin panel for management

    1. Selecting framework: Django / Laravel
    2. CRUD for games

  2. Import-export feature: loading extra data

  3. Data preparation

  4. Planning store UI

    1. Catalog page with search and filters
    2. Game detail page
    3. Basic user session or login

  5. How recommendations work in general

    1. Popularity-based: simple, strong baseline.
    2. Content-based: genres or TF-IDF with cosine similarity.
    3. (Stretch) Collaborative and hybrid: introduced in Phase 5.

  6. DB seeding

  7. Popularity-based engine

    1. Rank games by purchases or views
    2. Or average rating (a weighted score like Bayesian average so one 5-star review doesn’t win)
    3. Show it as a “Top games” or “Trending” block

  8. Measure it early!

    1. Define simple marketing metrics early
    2. Cick-through rate on recommendations
    3. Precision@k so every later step can be compared to this baseline

  9. Turning data into TF-IDF vectors

    1. Convert descriptions, genres, and tags into TF-IDF vectors.
    2. Embeddings are a later upgrade

  10. Similar items subsystem

    1. Compute cosine similarity between games and show “Similar games” on each detail page
    2. Precompute and cache the top-N neighbors per game

  11. Personalized “For you” block

    1. Build a user profile from the vectors of games they liked or bought, then recommend the nearest items

  12. Collecting feedback from interactions: impressions (what was shown), clicks, wishlist adds, purchases, ratings

    1. Log impressions (what was shown)
    2. Clicks
    3. Wishlist adds
    4. Purchases
    5. Ratings with timestamps

  13. Adding a bit of randomity...

    1. Because you only learn about what you show, mix in some random or less-shown items (e.g. epsilon-greedy: 10% of slots go to exploration)

  14. Scaling: adding more data, introducing new models

  15. Fighting common pitfalls

    1. Popularity bias
    2. Feedback loops
    3. Scaling
    4. Cold start   
Game recomendation engine: admin panel, db seeding, user context