数据稀疏背景下基于协同过滤的推荐算法综述

Research on data sparsity based on collaborative filtering recommendation algorithm

信息网络的飞速发展让人们能够高效处理工作,享受便利生活,但信息过载也带来了新的困扰。推荐系统的出现为用户从海量信息中筛选了感兴趣的内容,从而为企业提升了营销效率。文章通过研究基于协同过滤的推荐方法,分析对比几种算法的优劣,整理了推荐领域常用的相似度和数据集。最后,总结协同过滤推荐面临的难题,梳理其解决方法和对策,并讨论了其未来研究方向。

The rapid development of information network enables people to deal with work efficiently and enjoy convenient life,but information overload also brings new troubles.The emergence of recommendation system for users from the mass of information to screen the content of interest for enterprises to improve the efficiency of marketing.By studying the recommendation methods based on collaborative filtering,the advantages and disadvantages of several algorithms are analyzed and compared,and the similarity and data sets commonly used in the recommendation field are sorted out.Finally,the difficulties faced by collaborative filtering recommendation are summarized,the solutions and countermeasures are put forward,and the future research direction is discussed.