Context-Aware News Recommendation System: Incorporating Contextual Information and Collaborative Filtering Techniques

With the increasing volume of news articles available on the internet, personalized news recommendations have become increasingly important for users to discover relevant and interesting news articles. However, traditional recommender systems often fail to capture the dynamic nature of users' preferences and the changing trends in news articles. To address this challenge, this paper proposes a context-aware personalized news recommendation system that incorporates contextual information to enhance the personalization of news recommendations. The approach involves collecting, extracting, exploring, cleaning, and processing a large dataset of news articles from 19 distinct internet news sources, totaling 22,657 English pieces. Four different recommender systems were built using different techniques, including content-based methods such as TF-IDF, Bag-of-Words, and Word2Vec, and a collaborative filtering system based on click behavior. To evaluate the effectiveness of our models, we used a combination of standard comparison metric, including precision and recall, and user feedback. To demonstrate the applicability of the model, a web interface was constructed, and we used RMSE and MAE to evaluate the performance of the collaborative filtering model. In addition, we conducted a comparative study to compare the accuracy of different algorithms with different baseline methods, including random and recency. The evaluation results showed that incorporating contextual information and collaborative filtering can significantly improve the personalization of news recommendations. The study suggests that the collaborative filtering model based on click behavior is the most effective approach, with a mean MAE of 0.0252 and a mean RMSE of 0.0364. The content-based models were also effective approaches for recommending news articles, outperforming the baseline approaches.