NDLI: Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream

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Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream

Content Provider	ACM Digital Library
Author	Bolshinsky, Ella Daniel, Tal Guy, Ido Levin, Roy
Abstract	Social streams allow users to receive updates from their network by syndicating social media activity. These streams have become a popular way to share and consume information both on the web and in the enterprise. With so much activity going on, filtering and personalizing the stream for individual users is a key challenge. In this work, we study the recommendation of enterprise social stream items through a user survey with 510 participants, conducted within a globally distributed organization. In the survey, participants rated their level of interest and surprise for different items from the stream and could also indicate whether they were already familiar with the item. Thus, our evaluation goes beyond the common accuracy measure and examines aspects of serendipity and novelty. We also inspect how various features of the recommended item, its author, and reader, influence its ratings. Our results shed light on the key factors that make a stream item valuable to its reader within the enterprise.
Starting Page	665
Ending Page	674
Page Count	10
File Format	PDF
ISBN	9781450336215
DOI	10.1145/2766462.2767746
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Beyond accuracy Serendipity Social streams Social media Enterprise Surprise Recommender systems Novelty
Content Type	Text
Resource Type	Article

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