News Personalization using Support Vector Machines
Any type of content formally published in an academic journal, usually following a peer-review process.
We describe a system for recommending news articles, called NewsPer, which learns news-reading preferences of its users and suggests recently published articles that may be of interest to specific readers based on their interest profiles. The underlying algorithm is based on representing articles by bags of words and named entities, and applying support vector machines to this representation. We present this algorithm and give initial empirical results. We also discuss broader issues in the news personalization and the challenges of performance evaluation based on historical data.