An Efficient Multi-threaded Collaborative Filtering Approach in Recommendation System
Mahamudul Hasan

TL;DR
This paper introduces a multi-threaded collaborative filtering method that significantly speeds up recommendation systems by parallelizing user similarity computations, enhancing scalability and efficiency while maintaining data security.
Contribution
It presents a novel multi-threaded approach to collaborative filtering that reduces processing time and improves scalability in recommendation systems.
Findings
Reduced computation time compared to traditional methods
Enhanced scalability of recommendation systems
Maintained user data security during parallel processing
Abstract
Recommender systems are a subset of information filtering systems designed to predict and suggest items that users may find interesting or relevant based on their preferences, behaviors, or interactions. By analyzing user data such as past activities, ratings, and preferences, these systems generate personalized recommendations for products, services, or content, with common applications including online retail, media streaming platforms, and social media. Recommender systems are typically categorized into three types: content-based filtering, which recommends items similar to those the user has shown interest in; collaborative filtering, which analyzes the preferences of similar users; and hybrid methods, which combine both approaches to improve accuracy. These systems enhance user experience by reducing information overload and providing personalized suggestions, thus increasing…
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Taxonomy
TopicsRecommender Systems and Techniques
