A news recommendation system not only must recommend the latest, trending and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level and it is 7.9 on the scale of 10.
In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.
This paper investigates the Scandinavian daily press’ efforts in and
perspectives on algorithmic news recommendation. News recommender systems provide news
organisations with new opportunities to offer more relevant and personalised news
experiences, but their increasing use has also raised several concerns about whether and how
algorithms should undertake important editorial decisions. Current literature offers only
limited empirical insight into the actual use of these technologies in journalism, and this
paper is the first to map the use of news recommender systems in the Scandinavian media
system. Drawing on interviews with all 19 national newspapers within the Scandinavian daily
press, the findings reveal that 17 newspapers use news recommender systems and 14 of these
use personalisation. Most newspapers expressed positive attitudes toward the technologies,
highlighting increased relevance and better opportunities to drive subscriptions. The extent
of the use of news recommendation at the specific news media organisations is still limited
due to concerns about algorithms interfering with journalistic priorities and a reluctance
to jeopardise the brand value of the front page. Some newspapers address these concerns by
allowing for editorial control through subjectively estimated journalistic input, revealing
that journalistic norms and ideals affect the design and implementation of algorithms in
Nowadays with the development of technology and access to the Internet everywhere for everyone, the interest to get the news from newspapers and other traditional media is decreasing. Therefore, the popularity of news websites is ascending as the newspapers are changing into electronic versions. News websites can be accessed from anywhere, i.e., any country, city, region, etc. So, the need to present the news depends on where the reader is from can be a research area, as with facing with variety of news topics on websites readers prefer to choose those which more often show the news, they are interested in on their home pages. Based on this idea we represent the technique to find favorite topics of Twitter users of certain geographical districts to provide news websites a way of increasing popularity. In this work we processed tweets. It seems that tweets are some small data, but we found out that processing this small data needs a lot of time, due to the repetition of the algorithm a lot and many searches to be done. Therefore, we categorized our work as big data. To help this problem we developed our work in the Spark framework. Our technique includes 2 phases; Feature Extraction Phase and Topic Discovery Phase. Our analysis shows that with this technique we can get the accuracy between 68% and 76%, in 3 developments 3-fold, 5-fold, and 10-fold.