Hybrid Recommender for Condensed Sinhala News with Grey Sheep User Identification

Abstract

With the present explosion of news circulating the digital space, which consists mostly of unstructured textual data, there is a need to absorb the content of news easily and effectively. While there are many Sinhala news sites out there, no site facilitates recommendation despite the popularity of recommender systems in the current age and day. Therefore, it is effective if the news were presented in a summarized version which tallies with the user preferences as well. Our research aims to fill these gaps by providing a centralized news platform that recommends news to its users clearly and concisely. The news articles were collected using web scraping and after performing categorization it will be presented in a summarized context. Also, we expect to detect the grey sheep users and to provide separate recommendations to them in order to minimize errors in the recommendation. By implementing the proposed system, we provide a user-friendly Sinhala news platform.