Hybrid News Recommendation System using TF-IDF and Similarity Weight Index

C. P. Patidar, Yogesh Katara*, Meena Sharma
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Abstract

As the usage of internet is increasing, we are getting more dependent on it in our daily life. The Internet plays an essential role to simplify our tight schedules. In such tough lives, it is very important to stay aware of current affairs. Now for different people coming from different backgrounds and professions, the preferences are different too. Here come Data mining techniques in the picture, which gives us “Recommender system” as the output, capable of delivering more relevant and worthy outcomes. Newspapers are the basic obligation asked by almost every person to stay updated and aware of the world. But as we observe that nowadays, various solutions are been developed to convert paper news system to digital news and raise the bar of the quick news. And that’s how News Recommender systems are have made an important place in our fast running lives.This research paper has investigated the News Recommendation solution right from its core, including the importance, performance, and improvement suggestions. This paper talks about enhancing the performance of states solution by using modified Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. Proposed solution advocates the usage of JAVA technology which reflects fruitful results in the final graphs of accuracy, precision, and F-score. Here, BBC dataset has been used for comparison study purposes.
基于TF-IDF和相似权重指数的混合新闻推荐系统
随着互联网的使用越来越多,我们在日常生活中越来越依赖它。互联网在简化我们紧张的日程安排方面发挥着重要作用。在如此艰难的生活中,了解时事是非常重要的。对于来自不同背景和职业的人来说,他们的偏好也不同。这就是数据挖掘技术,它为我们提供了“推荐系统”作为输出,能够提供更多相关和有价值的结果。报纸是几乎每个人都要求的基本义务,以保持更新和了解世界。但我们观察到,如今,各种解决方案被开发出来,将纸质新闻系统转换为数字新闻,提高了快速新闻的标准。这就是新闻推荐系统在我们快节奏的生活中占据重要地位的原因。本文从新闻推荐解决方案的核心出发,对其重要性、性能和改进建议进行了研究。本文讨论了使用改进的词频-逆文档频率(TF-IDF)算法来提高状态解的性能。建议的解决方案提倡使用JAVA技术,该技术在准确度、精度和F-score的最终图表中反映出富有成效的结果。这里,BBC数据集被用于比较研究目的。
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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