Dataset : Kaggle : Amazon kindle reviews
The aging technique preserves actual ratings while considering time factors. It involves:
- Assigning more weight to recent reviews.
- Applying a decay factor based on review recency.
- Calculating a new overall rating that balances time and rating.
Matrix factorization addresses challenges in recommendation systems:
Matrix factorization, especially using Singular Value Decomposition (SVD), uncovers:
- Hidden factors influencing user preferences and item features.
- Identification of complex patterns, enhancing recommendation accuracy.
Matrix factorization, specifically SVD, achieves:
- Efficiently reduces dimensions of user-product matrix.
- Accelerates calculations without losing key insights.
Matrix factorization excels in handling sparse data:
- Predicts missing ratings using learned latent factors.
- Enhances recommendation quality by filling data gaps.
- In matrix factorization, user-user similarity measures how much users resemble each other.
- It considers their interaction patterns, behaviors, and preferences.
- The similarity is determined by comparing user ratings and preferences.
- It identifies users who share similar tastes and preferences.
- This enables personalized recommendations by suggesting items liked by users with comparable preferences.
- Product similarity in matrix factorization evaluates how items relate to each other based on user interactions.
- The system analyzes user ratings and interactions to identify items with shared characteristics or attributes.
- This similarity concept helps suggest items that are similar to ones a user has already shown interest in.
- By offering related item recommendations, it broadens the user's options and enhances their choices.
Matrix factorization is ideal for recommendation systems because it leverages both user-user and product similarity to generate accurate and personalized recommendations. By learning latent factors that capture intricate user-item interactions, it uncovers hidden patterns and preferences, leading to high-quality suggestions. This comprehensive approach capitalizes on collaborative filtering and latent features, enabling the system to adapt to user behaviors and preferences, ultimately enhancing the user experience.
Hackathon : Flipkart Grid 5.0
Team Members : @Spongy01 and @rachitshah07