Auto-Scoring of Personalised News in the Real-Time Web: Challenges, Overview and Evaluation of the State-of-the-Art Solutions

Abstract

The problem of automated personalised news recommendation, often referred as auto-scoring has attracted substantial research throughout the last decade in multiple domains such as data mining and machine learning, computer systems, e commerce and sociology. A typical "recommender systems" approach to solving this problem usually adopts content-based scoring, collaborative filtering or more often a hybrid approach. Due to their special nature, news articles introduce further challenges and constraints to conventional item recommendation problems, characterised by short lifetime and rapid popularity trends. In this survey, we provide an overview of the challenges and current solutions in news personalisation and ranking from both an algorithmic and system design perspective, and present our evaluation of the most representative scoring algorithms while also exploring the benefits of using a hybrid approach. Our evaluation is based on a real-life case study in news recommendations.