Collaborative Filtering (finding similar users/items based on past behavior), Content-Based Filtering (recommending items with similar features to what a user liked), and Hybrid Systems (combining both for better results). Other advanced types include Knowledge-Based, Deep Learning, and Demographic systems, which leverage different data points like item attributes, complex patterns, or user demographics to provide personalized suggestions.
Collaborative Filtering (CF):- Concept: "People who liked X also liked Y." It finds patterns in user-item interactions (ratings, purchases).
- Sub-types: User-based (If User A buys Item A, and a neighboring User B (who is found to be highly similar to User A based on their shared past interactions like rating or purchases) also buy Item B but User A has not yet bought it, then the system should recommend Item B to User A.) and Item-based (If a user likes item A, and many users who liked item A also liked item B, then the system should recommend item B to that user.).
- Techniques: Matrix Factorization (like SVD), Nearest Neighbors.
Content-Based Filtering:
- Concept: Recommends items similar to those a user has liked before, based on item features (e.g., movie genre, director, actors). If a user liked item A, they will also like item B if item B shares many of the same features as item A.
- How it works: Builds a user profile from features of liked items, then matches it to other items based on similarity. The features can also include those of the user like age and gender.
Hybrid Recommendation Systems:
- Concept: Merges CF and Content-Based methods, or other techniques, to overcome individual limitations (like the cold-start problem).
--Gemini
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
LightFM can solve cold-start problem: For a new user (cold-start user), LightFM uses their provided features (e.g., "age: 25, gender: female") to do content-based filtering to recommend items that share similar features with the user's profile. As the cold-start user interacts with items, the model gradually updates their information, shifting from pure content-based to a more personalized collaborative prediction.
