Neighborhood-based approaches in recommender systems are collaborative filtering techniques that predict user preferences by analyzing relationships between users or items. These methods rely on the idea that users with similar past behavior will have similar preferences in the future, or that items with similar characteristics will appeal to the same users. There are two primary types: user-user collaborative filtering, which identifies users with comparable tastes, and item-item collaborative filtering, which focuses on similarities between items. For example, if User A and User B both liked Movies X and Y, the system might recommend Movie Z to User A if User B also liked it.
A key strength of neighborhood methods is their simplicity and interpretability. User-user approaches calculate similarity metrics (e.g., cosine similarity or Pearson correlation) between users based on their ratings, then aggregate preferences from the most similar “neighbors.” Item-item methods, like Amazon’s “customers who bought this also bought” feature, compare items using co-occurrence patterns or rating similarities. For instance, if users who buy a specific camera often purchase a particular lens, the system will suggest the lens to others buying the camera. These approaches work well with explicit feedback (ratings) but can also handle implicit signals like views or clicks.
However, neighborhood methods face challenges with scalability and data sparsity. Calculating pairwise similarities becomes computationally expensive as the number of users or items grows into millions. Techniques like dimensionality reduction or approximate nearest neighbor algorithms (e.g., k-d trees) help mitigate this. Data sparsity—where most user-item interactions are missing—can lead to unreliable similarity calculations. Hybrid approaches, such as combining neighborhood methods with matrix factorization (e.g., SVD), often improve performance by capturing both local (neighborhood) and global patterns. Despite their limitations, these methods remain widely used due to their transparency and effectiveness in scenarios where explainable recommendations matter, such as e-commerce or content platforms.