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  4. Understanding User Behavior for Document Recommendation

Understanding User Behavior for Document Recommendation

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3366423.3380071.pdf

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Published version
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sword-2021-09-21T12:35:43.original.xml (130 B)
Original SWORD entry document
Author(s)
Xu, X
•
Hassan Awadallah, A
•
Dumais, S
•
Omar, F
•
Popp, B
•
Rounthwaite, R
•
Jahanbakhsh, Farnaz
Date Issued
April 2020
Journal
The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
Publisher
ACM
Version
Final published version
Abstract
© 2020 ACM. Personalized document recommendation systems aim to provide users with a quick shortcut to the documents they may want to access next, usually with an explanation about why the document is recommended. Previous work explored various methods for better recommendations and better explanations in different domains. However, there are few efforts that closely study how users react to the recommended items in a document recommendation scenario. We conducted a large-scale log study of users' interaction behavior with the explainable recommendation on one of the largest cloud document platforms office.com. Our analysis reveals a number of factors, including display position, file type, authorship, recency of last access, and most importantly, the recommendation explanations, that are associated with whether users will recognize or open the recommended documents. Moreover, we specifically focus on explanations and conduct an online experiment to investigate the influence of different explanations on user behavior. Our analysis indicates that the recommendations help users access their documents significantly faster, but sometimes users miss a recommendation and resort to other more complicated methods to open the documents. Our results suggest opportunities to improve explanations and more generally the design of systems that provide and explain recommendations for documents.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/133835.2
DOI of Published Version
https://doi.org/10.1145/3366423.3380071
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