Recommending topics for web curation

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Title: Recommending topics for web curation
Authors: Saaya, ZurinaSchaal, MarkusRafter, RachaelSmyth, Barry
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Date: 1356998
Online since: 2019-07-11T08:39:58Z
Abstract: A new generation of curation services provides users with a set of tools to manually curate and manage topical collections of content. However, given curation is ultimately a manual effort, it still requires significant effort on the part of the curator both in terms of collecting and managing content. We are interested in providing additional assistance to users in their curation tasks, in particular when it comes to efficiently adding content to their collection, and examine recommender systems in an effort to automate this task. We examine a number of recommendation strategies using live-user data from the popular curation service.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Start page: 242
End page: 253
Series/Report no.: Lecture Notes in Computer Science; Volume 7899
Copyright (published version): 2013 Springer-Verlag Berlin Heidelberg
Keywords: CurationRecommendation systemMachine learning
DOI: 10.1007/978-3-642-38844-6_20
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Language: en
Status of Item: Peer reviewed
Is part of: Micarelli, A., Semeraro, G. (eds.). User Modeling, Adaptation, and Personalization. 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings
Conference Details: UMAP 2013: The 21st Conference on User Modeling, Adaptation and Personalization, Rome, Italy, 10-14 June 2013
ISBN: 978-3-642-38843-9
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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