Understanding User-Based Filtering
Автор: NextGen AI Explorer
Загружено: 2025-05-24
Просмотров: 124
Описание: @genaiexp User-based collaborative filtering is a method that makes predictions about a user's interests by leveraging the preferences of similar users. The process begins by identifying users who have historically rated items similarly. To determine this similarity, various measures such as cosine similarity or Pearson correlation are often employed. Once similar users are identified, their ratings are aggregated to predict the unknown ratings for the target user. This approach is commonly used in online platforms where user interaction data is abundant. One of the advantages of user-based filtering is its straightforwardness; it’s intuitive and easy to implement. However, it may suffer from scalability issues as the number of users grows, and it can be less effective in sparse datasets. Despite these challenges, user-based filtering plays a crucial role in many systems we interact with daily, from e-commerce platforms recommending products to streaming services suggesting new content based on social proofs.
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