Steps to Build a KNN-Based Recommender System

1. Data Collection and Preprocessing

Collect data on user interactions with items, such as ratings, purchases, or clicks. Preprocess the data to handle missing values, normalize ratings, and transform the data into a user-item matrix.

2. Similarity Computation

Calculate the similarity between users or items using a similarity metric. Common metrics include:

• Cosine Similarity: Measures the cosine of the angle between two vectors.
• Pearson Correlation: Measures the linear correlation between two vectors.
• Jaccard Similarity: Measures the similarity between two sets.

3. Finding Neighbors

For each user or item, find the K-nearest neighbors based on the computed similarity scores. This involves sorting the similarity scores and selecting the top K neighbors.

4. Generating Recommendations

Based on the preferences of the K-nearest neighbors, generate recommendations for the user. This can be done by aggregating the ratings or interactions of the neighbors and recommending items with the highest aggregated scores.

Recommender systems are widely used in various applications, such as e-commerce, entertainment, and social media, to provide personalized recommendations to users. One common approach to building recommender systems is using the K-Nearest Neighbors (KNN) algorithm. This method leverages the similarity between users or items to generate recommendations.

Overview of K-Nearest Neighbors (KNN)

KNN is a simple, non-parametric, and instance-based learning algorithm that can be used for classification and regression tasks. In the context of recommender systems, KNN is used to find the closest neighbours (either users or items) based on a similarity metric. The recommendations are then made based on the preferences of these neighbours.

Types of Recommender Systems

1. User-Based Collaborative Filtering: This approach recommends items to a user by finding similar users (neighbours) who have similar preferences.
2. Item-Based Collaborative Filtering: This approach recommends items based on the similarity between items. It finds items similar to those the user has liked in the past.