Competition Phases
Primary Phase: Movie Recommendation
Objective: Develop an automated system to recommend 5 movies for users who have reviewed at least 10 movies on IMDb.
Focus Areas:
- Build personalized recommendations using historical review patterns
- Leverage user preferences and behavioral data
Evaluation Metrics:
- Recall@5, Recall@3, Recall@1
- Any other metric of your choice
Bonus Phase: Rating Prediction
Objective: Develop an automated system to predict IMDb user ratings for movies based on movie attributes.
Focus Areas:
- Train regression model using historical reviews
- Predict how a user might rate an unseen movie
Evaluation Metrics:
- Root Mean Squared Error (RMSE)
- Mean Squared Error (MSE)
- Any other metric of your choice
Important Dates
| Phase | Milestone | Date |
|---|---|---|
| Primary | Competition Starts | September 23, 2025 |
| Testing round Starts | November 10, 2025 | |
| Final Submission | November 20, 2025 | |
| Bonus | Competition Starts | November 22, 2025 |
| Testing round Starts | November 27, 2025 | |
| Final Submission | November 30, 2025 | |
| Results | Result Announcement and Prize Giving Ceremony | December 4, 2025 |
Eligibility
Students
Must be current IUB students
Team Size
Individual or teams up to 3 members
Faculty Support
Up to 2 faculty supervisors (IUB only)
Prizes & Recognition
Cash Prizes
- 1st Prize৳15,000
- 2nd Prize৳10,000
- 3rd/4th/5th Prize৳5,000 each
Additional Benefits
- Online certificates for all valid submissions
- Collaboration opportunities with MarriageChime and SimpliSolve LLC
- Recognition at prize giving ceremony
- Featured in future projects
Dataset
Primary Phase Dataset: Movie Data
Source: IMDb scraped data + IMDb Top 250
Attributes include:
- Title, poster, release year, duration, IMDb rating
- Genre, synopsis, user ratings, popularity score
- Director, writer, cast
- User reviews, critic feedback, awards
- Metacritic score, filming location, production info
Validation Set: MovieLens Latest Small Dataset (included) serves as gold standard for evaluation.
Evaluation & Submission
Submission Requirements
- Trained models + predictions (in specified format)
- Evaluation on private test dataset to prevent overfitting
- Submission instructions and format will be announced later
Judging Criteria
- Model performance on private test set
- Novelty and clarity of approach
- Technical innovation and methodology
Important: Evaluation will be conducted on a private test dataset, so generalization is key. Participants must submit both predictions and trained models. The code must be reproducible on other computers. We recommend attaching a “README.txt” with the code, with clear instructions and descriptions.
Rules & Guidelines
Allowed
- Any ML technique (collaborative filtering, deep learning, NLP)
- Google Colab (Free/Pro), Kaggle Notebooks (Free GPUs for 30 hrs/week)
- Local or personal setups
- Pretrained models or external data (if properly cited and open-source)
Prohibited - Will Result in Disqualification
- External proprietary data without disclosure
- Plagiarism or model tampering
- Violation of IMDb's Terms of Use
- Overfitting to public test data
License & Ethical Use
- Dataset shared for educational and research purposes only
- Sourced from IMDb.com - must comply with IMDb's Terms of Use
- Redistribution or commercial usage is strictly prohibited
Contact & Support
If you have any questions or need help:
Email: talenthunt@marriagechime.com
Competition Support: talenthunt@marriagechime.com
aiinnovationtalenthunt@gmail.com