A landscape view of news recommendation systems based on MIND dataset

Niran A. Abdulhussein, Ahmed J. Obaid
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Abstract

Nowadays, it’s a very important way for researchers and all people to find their desired meaning instead of searching for a specific topic. Recommendation systems are strategies to solve the problems of search, finding or reducing the time of interest content for users under complex information environments. R.S. can show us the related results that are close to what we desire. In this paper, we list rates of the most used techniques applied over MIND dataset and show the results and comparisons among these techniques, we have proposed our analysis on that dataset, which has been collected from Microsoft in 2019, and we proposed new techniques and explain the views of researchers in previous studies and the techniques in R.S. that depend on.
基于MIND数据集的新闻推荐系统全景图
如今,对于研究人员和所有人来说,找到他们想要的意义而不是搜索特定的主题是一种非常重要的方式。推荐系统是解决复杂信息环境下用户搜索、发现或减少感兴趣内容时间问题的策略。rs可以向我们展示接近我们期望的相关结果。在本文中,我们列出了在MIND数据集上应用的最常用技术的比率,并显示了这些技术之间的结果和比较,我们提出了对该数据集的分析,该数据集于2019年从微软收集,我们提出了新技术,并解释了研究人员在以前的研究中的观点和R.S.中依赖于的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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