Real-Time News Recommender System
Abstract
In this demo we present a robust system for delivering real-time news recommendation to the user based on the user’s history of the past visits to the site, current user’s context and popularity of stories. Our system is running live providing real-time recommendations of news articles. The system handles overspecializing as we recommend categories as opposed to items, it implicitly uses collaboration by taking into account user context and popular items and, it can handle new users by using context information. A unique characteristic of our system is that it prefers freshness over relevance, which is important for recommending news articles in real-world setting as addressed here. We experimentally compare the proposed approach as implemented in our system against several state-of-the-art alternatives and show that it significantly outperforms them.
Cite
Text
Fortuna et al. "Real-Time News Recommender System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_38Markdown
[Fortuna et al. "Real-Time News Recommender System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/fortuna2010ecmlpkdd-realtime/) doi:10.1007/978-3-642-15939-8_38BibTeX
@inproceedings{fortuna2010ecmlpkdd-realtime,
title = {{Real-Time News Recommender System}},
author = {Fortuna, Blaz and Fortuna, Carolina and Mladenic, Dunja},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {583-586},
doi = {10.1007/978-3-642-15939-8_38},
url = {https://mlanthology.org/ecmlpkdd/2010/fortuna2010ecmlpkdd-realtime/}
}