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Understanding User Behavior for Document Recommendation
Name
3366423.3380071.pdf
Description
Published version
Size
2.35 MB
Format
Adobe PDF
Checksum (MD5)
e1e86a28b78fa02a10c8e166303e9033
Author(s)
Xu, X
Hassan Awadallah, A
Dumais, S
Omar, F
Popp, B
Rounthwaite, R
Jahanbakhsh, F
Date Issued
April 20, 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.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.1145/3366423.3380071
