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.
Highlights
The process of data extraction is called Data Mining
From the past a few years, we have observed that personalization is taken on a whole new level and the user is assisted with more precise and overload data according to their preferences.This works advocates the Revised Manuscript Received on October 05, 2020. * Correspondence Author
JAVA technology is used in the evaluation and implementation of this work
Summary
The process of data extraction is called Data Mining. Later, this technique is used by different algorithms as their raw feed to result out better outcomes with précised and more relevant content. Recommendation system provides the user with the content they prefer to learn about For this purpose, this system processes both separate and specialized set of data. Incorporation of Data mining & Recommendation system, which can help in fetching the processed data purely extracted from the user’s preference and today’s trend cluster. This solution provides a better system and better results with popularity factors and trend results. If a user is looking for news related to sports for a particular team, the system must recommend all respective news belonging to that word along with all co-relevant news based no currently searched topic. This paper wraps itself with conclusion and stating the references used
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