摘要
在互联网时代,1个可以智能的为用户进行筛选和推荐的性能良好的推荐系统是互联网用户所必需的。该文给出一种面向微信公众平台新文章的个性化推荐算法。对于微信公众平台刚刚发布的新文章,通过对文章作者以及文章本身潜力做分析,对文章进行预测评分和智能排序,并结合用户的兴趣取向,把最具有爆点潜力的、阅读量可能达到10 w＋的文章个性化地推荐给大众。实验结果证明,这种新的推荐算法可以达到智能推荐的效果,帮助用户预测和推荐爆点,大大节省了用户的信息检索时间。
In the internet era, a good performance recommendation system which could intelligently screen and recommend is necessary for internet users. This paper presents a personalized recommendation algorithm towards to the new articles of We Chat public platform. By analyzing the potential of the article and the characteristics of other articles wrote by the same author, predicted rating and intelligent sorting can be obtained by using the proposed algorithm. Based on these results, we can then recommend the most explosive potential articles and the readings which have already been read by more than 100 thousand times to the public to achieve personalized recommendations. Experimental results show that this new recommendation algorithm can achieve the effect of intelligent recommendation and help users predict the explosion point, saving the users＇ information retrieval time.
作者
刘涛
刘佐
LIU Tgo;LIU Zuo(Department of Electronie and Communication Engineering, North China Electric Power University, Baoding 071003, China;China Telecom Corporation Limited, Baoding 071000, China)
出处
《控制工程》
CSCD
北大核心
2018年第6期999-1006,共8页
Control Engineering of China
基金
国家自然科学基金资助项目(61302105)
中央高校基本科研业务费专项资金资助项目
关键词
个性化
推荐
预测
推荐算法
信息检索
Personalized
recommend
predict
recommendation algorithm
information retrieval
