Hybrid Recommendation System

Movie recommendations need to account for both item attributes and patterns in user preferences.

An individual role is not specified in the available project material.

Project implementation

• Content-based recommendations from movie attributes.
• Collaborative filtering from user-item interaction patterns.
• A workflow for producing top-N movie recommendations.

The project explores item-feature similarity and user-item interactions through content-based and collaborative recommendation methods.

Architecture & workflow

1. 01Movie + rating data
2. 02Data preparation
3. 03Content + collaborative models
4. 04Ranking
5. 05Top-N output

Technical challenges

• Working with item similarity and user interaction signals.
• Understanding data sparsity and cold-start constraints.

• Produced top-N movie recommendations using content-based and collaborative methods.

• These are academic experiments on a MovieLens subset, with simulated ranking evaluation rather than external production validation.
• The report does not establish user impact or that the hybrid approach outperforms every individual model.

• Recommendation quality depends on the available item context and interaction data.

• Python
• Scikit-learn
• Machine Learning
• Recommendation Systems
• Data Analysis