个性化高校新闻分类推荐的应用研究

随着信息化和数字化时代的到来,大数据广泛渗入到各个领域。信息过载使得用户无法快速、准确地获取个人最关注的内容,这严重影响了浏览页面时的用户体验。为了能精准地进行分类推荐,对新闻分类和推荐方法进行细致的研究,进而提出一种能够进行精准分类,准确获取信息的推荐方法。利用爬虫技术获取真实的高校新闻数据;采用基于肘部法则改进的K-means算法进行聚类分析;结合用户注册时选定的兴趣标签,利用基于内容与协同过滤组合的推荐策略,针对不同用户的需求进行个性化推荐;以列表等形式将个性化的推荐结果展示给用户。根据高校的真实数据进行实验,结果表明,该算法能够有效地对高校师生的个性化服务需求进行处理,提高获取新闻时的高效性、准确性和智能性。

RESEARCH AND APPLICATION OF PERSONALIZED COLLEGE NEWS CLASSIFICATION AND RECOMMENDATION

With the appearance of the information and digital era,big data has been widely infiltrated into various fields. The overload information makes it impossible for users to get what they are most concerned about quickly and accurately,which seriously affects the users experience when browsing the pages. In order to make accurate classification recommendation and meet the users actual needs of obtaining the most concerned information quickly and accurately,the classification of news and recommended method was studied in detail,and then we put forward a kind of accurate classification to obtain the recommended method in this paper. Crawler technology was used to collect the real news data in colleges and universities,and then it based on the laws of the elbow K-means algorithm for clustering analysis. Then combining with the interest of users registered selected labels,based on the content and the combination of collaborative filtering recommendation strategy,we recommended individually for different users needs. The personalized page was presented in the form of a list or other forms to the users. The experimental data is from the real data in colleges and universities. The result of the research has shown that the improved algorithm can effectively infer the personalized service requirements of teachers and students in the universities.And it improves the efficiency,accuracy and intelligence of news acquisition.