基于过滤算法的网站针对性推荐模型

Website Targeted Recommendation Model Based on Filtering Algorithms

随着互联网技术的快速发展,个性化推荐系统在网站的用户粘性和用户体验方面发挥着重要作用。为提高网站针对性推荐的准确度与效率,提出结合过滤算法的网站针对性推荐模型。通过对用户行为数据进行深入分析,结合协同过滤算法对网站数据进行整合及深度挖掘。同时,通过相似度匹配更加准确地确定用户偏好,进行网站信息的针对性推荐。经过实验对比,基于过滤算法的网站针对性推荐模型比其他算法模型所需推荐时间更短,针对性推荐准确度更高,利于网站提升用户粘性与用户满意度。

With the rapid development of Internet technology,personalized recommendation system plays an important role in the user stickiness and user experience of websites.To improve the accuracy and efficiency of website targeted recommendations,a website targeted recommendation model combining filtering algorithms is proposed.Through in-depth analysis of user behavior data,combined with collaborative filtering algorithms,website data is integrated and deeply mined.Determine user preferences more accurately through similarity matching and provide targeted recommendations for website information.After experimental comparison,the website targeted recommendation model based on filtering algorithms requires shorter recommendation time and higher accuracy of targeted recommendations compared to other algorithm models,which is beneficial for websites to improve user stickiness and satisfaction.