Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories.

中文翻译：

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使用社交媒体挖掘的基于智能模糊规则的个性化新闻推荐。

由于用户兴趣类别随时间的变化，推荐相关且合适的新闻文章是一项必不可少但具有挑战性的任务。而且，互联网技术从大量资源中提供了丰富的新闻报道。同时，如今，许多人都在不考虑新闻站点的情况下通过社交媒体免费传播病毒新闻文章。因此，挖掘社交媒体来解决此类病毒新闻文章已成为另一个关键挑战。为了克服上述挑战，本文提出了一种模糊逻辑方法，通过分析用户的隐式用户资料来预测用户的多样化兴趣及其类别。根据用户的兴趣类别，通过挖掘社交媒体提要（Facebook和Twitter）来确定和分析病毒式新闻文章及其类别。此外，从结合了所检索的病毒新闻文章的新闻提要中检索新的新闻文章，所述病毒新闻文章作为关于用户的多样化兴趣的推荐而提供。所提出的用于预测所有类别的整体用户兴趣的方法的性能达到84.238％，而新闻提要，Facebook和Twitter的推荐准确度分别达到了用户兴趣类别的100％，90％和100％。