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 engagement and satisfaction. However, building a scalable recommendation system capable of handling numerous users efficiently is a significant challenge, particularly when considering both performance consistency and user data security, which are emerging research topics. The primary objective of this research is to address these challenges by reducing the processing time in recommendation systems. A multithreaded similarity approach is employed to achieve this, where users are divided into independent threads that run in parallel. This parallelization significantly reduces computation time compared to traditional methods, resulting in a faster, more efficient, and scalable recommendation system that ensures improved performance without compromising user data security.

翻译：推荐系统是信息过滤系统的一个子集，旨在根据用户的偏好、行为或交互来预测并推荐用户可能感兴趣或相关的项目。通过分析用户过去的活动、评分和偏好等数据，这些系统为产品、服务或内容生成个性化推荐，常见应用包括在线零售、媒体流平台和社交媒体。推荐系统通常分为三类：基于内容的过滤，推荐与用户已表现出兴趣的项目相似的项目；协同过滤，分析相似用户的偏好；以及混合方法，结合两种方法以提高准确性。这些系统通过减少信息过载并提供个性化建议来增强用户体验，从而提高参与度和满意度。然而，构建一个能够高效处理大量用户的可扩展推荐系统是一个重大挑战，特别是在考虑性能一致性和用户数据安全时，这些是新兴的研究课题。本研究的主要目标是通过减少推荐系统中的处理时间来应对这些挑战。为此采用了一种多线程相似性方法，将用户划分为独立线程并行运行。与传统方法相比，这种并行化显著减少了计算时间，从而实现了更快、更高效和可扩展的推荐系统，确保在不损害用户数据安全的情况下提升性能。