Addressing the Limitations of News Recommendation Systems: Incorporating User Demographics for Enhanced Personalization

EasyChair Preprint 10506

Abstract

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.

Keyphrases: Hybrid Approach, New Users, News Recommendation System, Popular News, cold-start problem, demographic information

@booklet{EasyChair:10506,
  author    = {Zerihun Olana and Admas Abtew},
  title     = {Addressing the Limitations of News Recommendation Systems: Incorporating User Demographics for Enhanced Personalization},
  howpublished = {EasyChair Preprint 10506},
  year      = {EasyChair, 2023}}