Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Learning to model relatedness for news recommendation
Proceedings of the 20th international conference on World wide web
From chatter to headlines: harnessing the real-time web for personalized news recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Improving news ranking by community tweets
Proceedings of the 21st international conference companion on World Wide Web
Social media-driven news personalization
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
PEN recsys: a personalized news recommender systems framework
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
Hi-index | 0.00 |
Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine-grained recommendation strategy. This demo paper presents the implementation of our solution to efficiently recommend specific news articles from a large corpus of newly-published press releases in a way that closely matches a reader's reading preferences.