News Personalization using Support Vector Machines
Figshare2018
Citations Over TimeTop 12% of 2018 papers
Abstract
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.
Related Papers
- → Neural News Recommendation with Attentive Multi-View Learning(2019)247 cited
- → Neural News Recommendation with Topic-Aware News Representation(2019)87 cited
- → News Recommendation with Topic-Enriched Knowledge Graphs(2020)53 cited
- → News recommendation via hypergraph learning(2013)95 cited
- → NewsRec, a Personal Recommendation System for News Websites(2005)2 cited