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2025年5月2日 16:04 星期五
首页 > 过刊浏览>2022年第31卷第4期 >47-58. DOI:10.15888/j.cnki.csa.008403
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智能推荐系统研究综述
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中国高等教育学会专项课题(2020JXD01); 广东省普通高校“人工智能”重点领域专项(2019KZDZX1027); 广东高校省级重点平台和重大科研项目(2017KTSCX048); 广东省公益研究与能力建设(2018B070714018); 广东省中医药局科研项目(20191411); 广东省高等学校产业学院建设项目(人工智能机器人教育产业学院)


Survey on Intelligent Recommendation System
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    摘要:

    伴随着电子商务平台和新型数字媒体服务迅速发展, 网络数据规模持续增长, 数据类型呈现多样化, 如何从大规模数据中挖掘有价值的信息, 已经成为信息技术的一项巨大挑战. 推荐系统能够缓解“信息过载”问题, 挖掘数据潜在价值, 将个性化信息推送给有需要的用户, 提高信息利用率. 深度学习的表征能力与推荐系统相融合, 有助于深层次地挖掘用户需求, 提供精准的个性化推荐服务. 本文首先分析传统推荐算法的优缺点, 再总结深度学习技术在推荐系统中的研究进展. 最后, 分析和展望智能推荐系统未来发展方向.

    Abstract:

    With the rapid development of e-commerce platforms and new digital media services, the scale of network data continues to grow and data types are diversified. The mining of valuable information from large-scale data has become a huge challenge for information technology. Recommendation systems can alleviate the “information overload” problem, explore the potential value of data, push personalized information to users in need, and improve information utilization. The combination of the representational capabilities of deep learning and recommendation systems helps to dig deeper into user needs and provide accurate personalized recommendation services. This study analyzes the advantages and disadvantages of traditional recommendation algorithms, summarizes the research progress of deep learning technology in recommendation systems, and probes into the future development directions of intelligent recommendation systems.

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胡琪,朱定局,吴惠粦,巫丽红.智能推荐系统研究综述.计算机系统应用,2022,31(4):47-58

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  • 收稿日期:2021-06-11
  • 最后修改日期:2021-07-14
  • 在线发布日期: 2022-03-22
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