基于大数据的电子商务个性化推荐算法研究与合法性探析

Research and Legitimacy Exploration of Personalized Recommendation Algorithm for E-Commerce Based on Big Data

随着大数据技术的快速发展,电子商务个性化推荐系统已成为提升用户体验和企业效益的重要手段。本文旨在研究基于大数据的电子商务个性化推荐算法,并深入探讨其合法性问题,以期在保障用户隐私和数据安全的前提下,优化推荐效果,促进电子商务的健康发展。本文首先介绍了大数据技术在电子商务中的应用及其所带来的变革与挑战。接着,深入研究了当前主流的个性化推荐算法,包括协同过滤、基于内容的推荐、混合推荐以及深度学习在推荐系统中的应用。最后,本文聚焦于电子商务个性化推荐的合法性问题,分析了用户隐私保护与数据安全的法律法规要求,以及个性化推荐中隐私泄露的风险。在此基础上,提出了算法的优化方向及相应的合法性保障措施,这些举措旨在提升算法推荐的准确性和多样性,同时确保用户隐私和数据安全不受侵犯。

With the rapid development of big data technology, an e-commerce personalized recommendation system has become an important means to enhance user experience and enterprise benefits. The purpose of this paper is to study the e-commerce personalized recommendation algorithm based on big data and discuss its legitimacy in depth, with a view to optimizing the recommendation effect and promoting the healthy development of e-commerce under the premise of safeguarding user privacy and data security. This paper first introduces the application of big data technology in e-commerce and the changes and challenges it brings. Then, the current mainstream personalized recommendation algorithms are studied in depth, including collaborative filtering, content-based recommendation, hybrid recommendation, and the application of deep learning in the recommendation system. Finally, this paper focuses on the legitimacy of e-commerce personalized recommendation, analyzing the legal and regulatory requirements of user privacy protection and data security, as well as the risk of privacy leakage in personalized recommendation. On this basis, the optimization direction of the algorithm and the corresponding legitimacy safeguards are proposed, and these initiatives aim to improve the accuracy and diversity of the algorithmic recommendations, while ensuring that user privacy and data security are not violated.