A Personalised Reader for Crowd Curated Content.
RECSYS(2015)
摘要
ABSTRACTPersonalised 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.
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AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络