基于用户浏览行为的个性化推荐研究综述

研究如何充分利用海量用户浏览行为数据,构建更加精确的推荐算法和模型,以提高推荐系统性能,是目前个性化推荐领域研究的热点.针对这些问题,首先对用户的浏览行为进行了简要概括表述,给出了基于浏览行为推荐系统的总体框架,回顾总结了基于用户浏览行为的推荐系统的发展历程.对其关键技术和单一浏览行为量化方法与混合浏览行为量化方法进行总结、对比和分析.最后讨论了结合多源异构数据的浏览行为推荐的最新成果,总结了该领域未来研究难点和发展趋势.

Survey of personalized recommendation study based on user browsing behavior

In order to improve the performance of the recommendation system, the hot research of personalized recommendation field is how to build more accurate recommendation algorithms and models with massive user browsing behavior data. Firstly, this paper briefly described the browsing behavior of users, gave the overall framework of the browsing behavior recommendation system, and summarized the development of the recommendation system based on user browsing behavior. It summarized, compared and analyzed the key techniques and single browsing behavior quantization method and mixed browsing behavior quantization method. Finally, it discussed the latest achievements of browsing behavior recommendation combined with multi-source heterogeneous data and summarized future research difficulties and development trends in this field.