Paper Info
Title
Understanding User Behavior For Document Recommendation
Abstract
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.
Year
DOI
Venue
2020
10.1145/3366423.3380071
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020
Keywords
DocType
ISBN
Large Scale Log Analysis, User Behavior, Document Recommendation, Explanation
Conference
978-1-4503-7023-3
Citations 
PageRank 
References 
2
0.36
0
Authors
7
Name
Order
Citations
PageRank
Xuhai Xu1102.81
Ahmed Hassan294357.64
Susan Dumais3139482130.47
Farheen Omar420.36
Bogdan Popp520.36
Robert Rounthwaite631450.09
Farnaz Jahanbakhsh730.71