基于用户行为的新闻推荐算法的研究

为了提高新闻推荐的效率和准确度,减少相似内容的反复推荐,通过研究用户行为和分析用户新闻浏览行为日志,采用以马尔科夫算法为主要算法的新闻推荐算法,辅以协同过滤算法和基于内容的推荐算法,建立了马尔科夫模型,并应用在智能新闻推荐上。通过对比传统的推荐算法,测试结果表明,该模型在准确度和执行效率上有明显的提升,其功能更加智能。

A news recommendation algorithm based on user behavior

In order to improve the efficiency and accuracy of news recommendation and reduce the repeated recommendation of similar content, through the research of user behavior and the analysis of user's news browsing behavior log, a news recommendation algorithm with Markov algorithm as the main algorithm is adopted. The algorithm is supplemented by the collaborative filtering algorithm and the content-based recommendation algorithm to establish a Markov model, thus realizing the application in intelligent news recommendation. Test results show that, compared with the traditional recommendation algorithm, the algorithm significantly improves the accuracy and execution efficiency, and its function is more intelligent.