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

Published: 09 August 2015 Publication History

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.

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      cover image ACM Conferences
      SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
      August 2015
      1198 pages
      ISBN:9781450336215
      DOI:10.1145/2766462
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 09 August 2015

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      Author Tags

      1. beyond accuracy
      2. enterprise
      3. novelty
      4. recommender systems
      5. serendipity
      6. social media
      7. social streams
      8. surprise

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      • (2019)A Research Literature Study of Enterprise SocialMedia Platforms in OrganizationsBeta10.18261/issn.1504-3134-2019-01-0633:1(84-112)Online publication date: 7-May-2019
      • (2018)Connecting sellers and buyers on the world's largest inventoryProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241733(490-491)Online publication date: 27-Sep-2018
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      • (2017)Researching Serendipity in Digital Information EnvironmentsSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00790ED1V01Y201707ICR0599:6(i-91)Online publication date: 28-Sep-2017
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