KNN Classifier in Collaborative Filtering Recommender Systems

K-Nearest Neighbors (KNN) Classifier in Recommender Systems

Collaborative Filtering

• A method used to build recommender systems
• Based on the idea that users with similar preferences will like similar items
• Two types: User-based and Item-based

User-based Recommendation

• Recommends items to a user based on the preferences of similar users
• Finds the k most similar users to the target user
• Recommends items that the similar users have liked or purchased
• Uses a KNN classifier to find similar users

Item-based Recommendation

• Recommends items that are similar to the ones a user has already liked or purchased
• Finds the k most similar items to the target item
• Recommends these items to the user
• Uses a KNN classifier to find similar items

Evaluation Metrics

• Used to measure the performance of a recommender system
• Examples:
  • Precision: measures the number of relevant items in the top-N recommended items
  • Recall: measures the number of relevant items recommended out of all relevant items
  • F1-score: harmonic mean of precision and recall
  • Mean Average Precision (MAP): measures the average precision of the top-N recommended items

Similarity Metric

• Used to measure the similarity between users or items
• Examples:
  • Cosine similarity: measures the cosine of the angle between two vectors
  • Jaccard similarity: measures the similarity between two sets
  • Pearson correlation: measures the linear correlation between two vectors

One-hot Encoding

• A method used to convert categorical data into a numerical representation
• Each category is represented as a binary vector (0 or 1) indicating the presence or absence of the category
• Used to convert user or item features into a numerical format that can be used by the KNN classifier

Collaborative Filtering in Recommender Systems

• Collaborative Filtering is a method used to build recommender systems
• It's based on the idea that users with similar preferences will like similar items
• There are two types of Collaborative Filtering: User-based and Item-based

User-based Recommendation

• Recommends items to a user based on the preferences of similar users
• Finds the k most similar users to the target user using a KNN classifier
• Recommends items that the similar users have liked or purchased

Item-based Recommendation

• Recommends items that are similar to the ones a user has already liked or purchased
• Finds the k most similar items to the target item using a KNN classifier
• Recommends these items to the user

Evaluation Metrics for Recommender Systems

• Evaluation Metrics are used to measure the performance of a recommender system
• Examples of Evaluation Metrics include:
  • Precision: measures the number of relevant items in the top-N recommended items
  • Recall: measures the number of relevant items recommended out of all relevant items
  • F1-score: harmonic mean of precision and recall
  • Mean Average Precision (MAP): measures the average precision of the top-N recommended items

Similarity Metrics in KNN Classifier

• Similarity Metrics are used to measure the similarity between users or items
• Examples of Similarity Metrics include:
  • Cosine similarity: measures the cosine of the angle between two vectors
  • Jaccard similarity: measures the similarity between two sets
  • Pearson correlation: measures the linear correlation between two vectors

One-hot Encoding in KNN Classifier

• One-hot Encoding is a method used to convert categorical data into a numerical representation
• Each category is represented as a binary vector (0 or 1) indicating the presence or absence of the category
• Used to convert user or item features into a numerical format that can be used by the KNN classifier