Abstract
With the explosive growth of online news, adaptive news recommendation models based on social connections have developed as a result of the growth of online news websites. Several models have relayed better outcomes for users. Most of these models establish users' social connections according to their binary news rating scores. In various real systems, mainly based on a non-binary pattern, the rating score is often multigrade. In this paper, we introduce a new continuous ratings model for news recommendations. This model uses exponential distance to measure the similarities of users to increase its robustness to noise. Simulation results show that the proposed model has significantly improved the performance of personalized news recommendation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
