Website Targeted Recommendation Model Based on Filtering Algorithms
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.
user stickinesscollaborative filtering algorithmsimilarity matchingdata miningtargeted recommendation
万方数据
维普
