A Personalised Reader for Crowd Curated Content

Personalised news recommender systems traditionally rely on content ingested from a select set of publishers and ask users to indicate their interests from a predefined list of topics. They then provide users a feed of news items for each of their topics. In this demo, we present a mobile app that automatically learns users’ interests from their browsing or twitter history and provides them with a personalised feed of diverse, crowd curated content. The app also continuously learns from the users’ interactions as they swipe to like or skip items recommended to them. In addition, users can discover trending stories and content liked by other users they follow. The crowd is thus formed of the users, who as a whole act as the curators of the content to be recommended. Categories and Subject Descriptors H.4.m [Information Systems Applications]: Miscellaneous Keywords Recommender system, crowd curation, social filter, mobile

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Gabriella Kazai, Daoud Clarke, Iskander Yusof, Matteo Venanzi
• Keywords: Control Systems, RECSYS 2015