Analyzing the interaction of users with news articles to create personalization services

Abstract

News personalization technologies aim at providing contents tailored to the users preferences. While recommender systems provide suggestions to users by taking advantage of individual preferences, content of news portals can be tailored on the bases of sociological aspects (e.g., the demographics of the users or the region in which they live) elicited from user interactions with the news. This allows to generate personalization with a coarse granularity; however, no study has ever shown a large-scale analysis of how the users interact with the news, focusing on different user segments. This paper uses the Yahoo News Feed dataset, a corpus that contains more than 101 billion examples of interactions between users and news items. The data present in the corpus spans for a range of 4 months, and was extracted from real user interactions with news items in the Yahoo Web portal. The dataset has been analyzed in order to understand users behaviors and their relations with sociological aspects. Thanks to our analysis, different forms of personalization can be generated.