摘要
针对目前现有的新闻推荐系统未能充分考虑新闻的语义信息,对新闻文本建模因子的单一性问题,提出注意力与多视角融合的新闻推荐算法(Attention-BodyTitleEvent,Attention-BTE).利用BERT模型以及注意力机制分别对新闻标题、正文、事件向量化,将三者融合即新闻向量化表示,再对候选新闻和用户浏览新闻数据进行处理,分别得到对应的候选新闻向量化和用户向量化,并将其进行点乘得到用户点击候选新闻的概率,即新闻推荐结果.实验数据表明,与其他的新闻推荐算法相比,该模型在F1指标上提高了约6%.
The existing news recommendation system fails to sufficiently consider the semantic information of news,and modeling factors for news body suffers from unity problems.Attention-BodyTitleEvent(Attention-BTE),a news recommendation algorithm based on fusion of attention and multi-perspectives,is proposed in this study.The BERT model and attention mechanism are applied to vectorize the body,title,and event in the news respectively.The three parts are combined to represent news vectorization,and then the candidate news and user browsing news data are processed respectively to obtain the corresponding candidate news vectorization and user vectorization.Finally,dot multiplication is conducted to obtain the probability of users clicking on the candidate news,namely the news recommendation result.Experimental data demonstrate that Attention-BTE improves the index by about 6%compared with the other news recommendation algorithm.
作者
范琳娟
孙喁喁
徐飞
周行行
FAN Lin-Juan;SUN Yong-Yong;XU Fei;ZHOU Hang-Hang(School of Computer Science and Engineering,Xi’an Technological University,Xi’an 710021,China)
出处
《计算机系统应用》
2022年第12期178-186,共9页
Computer Systems & Applications
基金
新型网络与检测控制国家地方联合工程实验室项目(GSYSJ2016013)
关键词
新闻推荐系统
多视角
注意力机制
事件
向量化
个性化推荐
news recommendation system
multi-perspective
attention mechanism
event
vectorization
personalized recommendation
作者简介
通信作者:范琳娟,E-mail:1530196267@qq.com
