News Article Position Recommendation Based on the Analysis of Article's Content - Time Matters

As more people prefer to read news on-line, the newspapers are focusing on personalized news presentation. In this study, we investigate the prediction of article’s position based on the analysis of article’s content using different text analytics methods. The evaluation is performed in 4 main scenarios using articles from different time frames. The result of the analysis shows that the article’s freshness plays an important role in the prediction of a new article’s position. Also, the results from this work provides insight on how to find an optimised solution to automate the process of assigning new article the right position. We believe that these insights may further be used in developing content based news recommender algorithms.