research-article

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

Authors:

Ella BolshinskyAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 665 - 674

https://doi.org/10.1145/2766462.2767746

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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Haugsbakken H(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
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Index Terms

Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

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

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 09 August 2015

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Palomares IPorcel CPizzato LGuy IHerrera-Viedma E(2020)Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendationInformation Fusion10.1016/j.inffus.2020.12.001Online publication date: Dec-2020
https://doi.org/10.1016/j.inffus.2020.12.001
Haugsbakken H(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
https://doi.org/10.18261/issn.1504-3134-2019-01-06
Guy IPera SEkstrand MAmatriain XO'Donovan J(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
https://dl.acm.org/doi/10.1145/3240323.3241733
Zwerdling NRonen ILeiba LBarnea MMitrovic TZhang JChen LChin D(2018)Orient Me!Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209247(169-173)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209247
Guy I(2018)The Characteristics of Voice SearchACM Transactions on Information Systems10.1145/318216336:3(1-28)Online publication date: 13-Mar-2018
https://dl.acm.org/doi/10.1145/3182163
Guy I(2018)People Recommendation on Social MediaSocial Information Access10.1007/978-3-319-90092-6_15(570-623)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_15
McCay-Peet LToms E(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
https://doi.org/10.2200/S00790ED1V01Y201707ICR059
Tsurel DPelleg DGuy IShahaf Dde Rijke MShokouhi MTomkins AZhang M(2017)Fun FactsProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018709(345-354)Online publication date: 2-Feb-2017
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Lombardi IVernero F(2017)What and who withInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2017.01.001101:C(62-75)Online publication date: 1-May-2017
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Guy IPizzato LSen SGeyer WFreyne JCastells P(2016)People Recommendation TutorialProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959196(431-432)Online publication date: 7-Sep-2016
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