Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior

@article{Li2019PersonalisedRO,
  title={Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior},
  author={Xinyi Li and Yifan Chen and Benjamin Pettit and M. Rijke},
  journal={ACM Transactions on Information Systems (TOIS)},
  year={2019},
  volume={37},
  pages={1 - 23}
}
Academic search engines have been widely used to access academic papers, where users’ information needs are explicitly represented as search queries. Some modern recommender systems have taken one step further by predicting users’ information needs without the presence of an explicit query. In this article, we examine an academic paper recommender that sends out paper recommendations in email newsletters, based on the users’ browsing history on the academic search engine. Specifically, we look… Expand
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