NDLI: Mendeley: Recommendations for Researchers

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Mechanism Design for Personalized Recommender Systems

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The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems

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Mendeley: Recommendations for Researchers

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Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion

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Mendeley: Recommendations for Researchers

Content Provider	ACM Digital Library
Author	Jack, Kris Vargas, Saúl Hristakeva, Maya
Abstract	For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.
Starting Page	365
Ending Page	365
Page Count	1
File Format	PDF
ISBN	9781450340359
DOI	10.1145/2959100.2959116
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Research article recommendation People recommendation
Content Type	Text
Resource Type	Article

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