Project Overview
Recommendation engines help digital platforms personalize content and improve discovery. In this project, I designed a book recommendation workflow using collaborative filtering techniques to connect users with titles that match their historical preferences.
System Architecture
The core methodology relied on memory-based collaborative filtering, observing patterns in historical user behavior to predict future preferences.
- Data Structuring: Constructed a sparse user-item interaction matrix to map users against catalog items.
- Similarity Computation: Applied Cosine Similarity algorithms to calculate vector distances between users (User-Based) and items (Item-Based).
- Optimization: Applied sparsity-aware processing so recommendations could be generated efficiently from limited interaction data.
Impact and Application
By refining the underlying similarity logic, the system can produce relevant suggestions even when the interaction matrix is sparse. This is especially useful for recommendation tasks where many users rate or interact with only a small portion of the catalog.
While applied to books in this context, the same recommendation approach can be adapted for product suggestions, content discovery, and cross-selling analysis.