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Personalized activity streams: sifting through the "river of news"

Published: 23 October 2011 Publication History

Abstract

Activity streams have emerged as a means to syndicate updates about a user or a group of users within a social network site or a set of sites. As the flood of updates becomes highly intensive and noisy, users are faced with a "needle in a haystack" challenge when they wish to read the news most interesting to them. In this work, we study activity stream personalization as a means of coping with this challenge. We experiment with an enterprise activity stream that includes status updates and news across a variety of social media applications. We examine an entity-based user profile and a stream-based profile across three dimensions: people, terms, and places, and provide a rich set of results through a user study that combines direct rating of the objects in the profile with rating of the news items it produces.

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      cover image ACM Conferences
      RecSys '11: Proceedings of the fifth ACM conference on Recommender systems
      October 2011
      414 pages
      ISBN:9781450306836
      DOI:10.1145/2043932
      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: 23 October 2011

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      Author Tags

      1. Twitter
      2. activity streams
      3. news feed
      4. personalization
      5. real-time web
      6. recommender systems
      7. social media
      8. social networks
      9. social streams

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      RecSys '11
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      RecSys '11: Fifth ACM Conference on Recommender Systems
      October 23 - 27, 2011
      Illinois, Chicago, USA

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      Cited By

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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
      • (2019)An ontology‐based framework of assessment analytics for massive learningComputer Applications in Engineering Education10.1002/cae.2215527:6(1343-1360)Online publication date: 16-Aug-2019
      • (2018)Implicit User Modeling in Group ChatAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225236(275-280)Online publication date: 2-Jul-2018
      • (2018)The Rise of GuardiansThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210037(275-284)Online publication date: 27-Jun-2018
      • (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
      • (2018)People Recommendation on Social MediaSocial Information Access10.1007/978-3-319-90092-6_15(570-623)Online publication date: 3-May-2018
      • (2016)People Recommendation TutorialProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959196(431-432)Online publication date: 7-Sep-2016
      • (2015)The Role of User Location in Personalized Search and RecommendationProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2799502(236-236)Online publication date: 16-Sep-2015
      • (2015)Personalizing LinkedIn FeedProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2788614(1651-1660)Online publication date: 10-Aug-2015
      • (2015)Interaction-Based Recommendations for Online CommunitiesACM Transactions on Internet Technology10.1145/277497415:2(1-21)Online publication date: 24-Jun-2015
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