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A news article is not just a fact or information, but it is the information that affects people. It affects the way people live their lives, performs their jobs, and make decisions. A news article tells people what is happening around them that they must be aware of as a resident, a human being, a community member, and as a part of the socio-economic, biological and political system.
Online news readers frequently face information overload due to several news websites, and these news websites keep regularly increasing (Tiwari, 2018). From the tons of available news websites (or portals) online, containing hundreds of news articles from around the globe, what makes a news website stand out from others is how precisely and efficiently it delivers the news articles to the users. In addition, finding news relevant to the users is equivalent to finding a needle in a haystack.
Moreover, every individual is different from others in terms of taste, personality, and needs, and therefore their interest in news articles differs from others. Some people like to read articles about politics while some like sports, arts, technology, environmental changes, etc.
User needs to find the precise, trending and specific news which interest them. They may also be interested in the discussions and opinions of other people on the current news articles. However, due to the massive amount of information, it is tedious to find relevant news and discussions. There are several news aggregator apps available such as Apple News, News360m, Yahoo News, etc. which provide user with several news articles but they do none or a very little personalization of the contents. These apps generally use the static user profile or recent social media trends to notify the news article to the users (Tiwari, 2018). The news recommendation system (RS) may solve this problem by recommending personalized news from the most popular resources. A news recommendation system recommends the news article to the user by taking into various parameters provided to it as input.
The primary goal of a recommendation system is to learn the user behavior and preferences and generate the meaningful recommendations (Adomavicius, 2005; Burke, 2002). Under the hood, recommendation systems rely on various algorithms to determine what should be recommended. Some algorithms look at keywords or editorial tags to find matching content, while others analyze the content more deeply, on a semantic level. Some consider the diversity and novelty of the items in context, while others gather a range of tracking data to personalize recommendations to each user dynamically (Adomavicius, 2011; Adomavicius, 2005). However, the best systems leverage a hybrid of all of the above.
In current era, twitter has become an important source for disseminating the news and opinion across the globe. The news preferences of users are highly affected by the recent trends in social media. Therefore, the news article recommendations considering the latest twitter trends and user preferences will make it convenient for users to get relevant information. This will not only help users to gain knowledge about the current issues but also allow them to read the opinion and viewpoints of other people.