A recommender trained on your own ratings

Level 2: two to three weeks, real architecture. Use with: clean-a-dataset, train-a-model, build-web-app. Resume line when done: Built {project_name}, a recommender trained on {n} of my own ratings and tested on held-out picks — live at {url}

Brief

You have years of opinions about books or movies. Turn them into a dataset and find out whether a model can predict your next rating better than a dumb rule can. Collect your real ratings history, attach metadata for each title, train a simple model, and test it on ratings it never saw. The demo is a live page that ranks titles you have not tried yet, with the honest accuracy numbers next to it.

Personalize

• Your ratings. From your reading log, your movie-tracking account's export, or a spreadsheet you build from memory in one sitting. At least 60 titles, rated on a scale you define and stick to. It must be your own history.
• Your features. Genre, year, length, author or director, and whatever you suspect drives your taste. Write your suspicion down before training, then check whether the model agrees.
• Your baseline. Predicting your average rating for everything is the floor. A one-rule guess ("I rate everything in my favorite genre one point higher") is a stronger one. The model has to earn its place against both.

Milestones

1. Collect and clean the ratings: one row per title, your rating, and the date if you have it. Document every judgment call (rereads, ratings you no longer agree with).
2. Attach metadata for every title. Missing values get a policy, written in the README.
3. Chart your own taste first: ratings by genre, by year, by length. Write three sentences on what you see.
4. Hold out your 15 most recent ratings. They are the test set and nothing touches them until evaluation.
5. Compute both baselines on the held-out set: average-rating error and your one-rule error.
6. Train a simple model (regression or nearest-neighbors on your own history) and evaluate on the same held-out ratings. Report error next to the baselines.
7. Look at the five worst misses individually and write down what the model could not know.
8. Build the demo: a page listing unseen titles ranked by predicted rating, with the evaluation table on the same page.
9. Field test: pick the model's top recommendation, actually read or watch it, and publish your real rating next to the prediction.
10. Write the limitations section: 60 titles is small, your taste drifts, and a model of past-you may not fit future-you.

Done means

• Live page ranking unseen titles, with predicted ratings shown
• Evaluation on held-out ratings, reported next to both baselines on the same page
• If a baseline ties or wins, the page says so
• At least one field test: a recommendation you actually tried, with prediction and real rating side by side
• Worst misses shown with your explanation of each
• Limitations section written by you, no claim of knowing your taste beyond what the numbers support