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Automatic on-device filtering of social networking feeds

Published: 14 October 2012 Publication History

Abstract

Many people follow social networking services and find it difficult to locate essential content on mobile devices. Automatic filtering of the feeds is one solution to this problem. A system learns a model for each user, based on metadata (e.g., content types and contacts) and click histories for old feed items, predicts the click probability for incoming items, and automatically filters out less important ones. In this study, we implemented several alternative automatic filtering systems and evaluate their offline accuracy and user acceptance. 40 users completed the evaluation in a field study. Two main findings emerge from the study. Firstly, PageRank and Bayesian predictors are valid methods; an ensemble predictor combining the two further improves the prediction accuracy. Secondly, people show high acceptance of the automatic filtering function. The participants using the filtering function found it easier to access interesting content than did the participants without the filtering. On average, they also felt greater sense of control, due to the reduced feed volume.

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Cited By

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  • (2024)Contesting personalized recommender systems: a cross-country analysis of user preferencesInformation, Communication & Society10.1080/1369118X.2024.2363926(1-20)Online publication date: 3-Jul-2024
  • (2013)A novel mobile device user interface with integrated social networking servicesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2013.03.00471:9(919-932)Online publication date: 1-Sep-2013

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    cover image ACM Other conferences
    NordiCHI '12: Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design
    October 2012
    834 pages
    ISBN:9781450314824
    DOI:10.1145/2399016
    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: 14 October 2012

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    Author Tags

    1. filtering
    2. mobile device
    3. prediction accuracy
    4. social networking service
    5. user acceptance

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    NordiCHI '12 Paper Acceptance Rate 84 of 341 submissions, 25%;
    Overall Acceptance Rate 379 of 1,572 submissions, 24%

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    • (2024)Contesting personalized recommender systems: a cross-country analysis of user preferencesInformation, Communication & Society10.1080/1369118X.2024.2363926(1-20)Online publication date: 3-Jul-2024
    • (2013)A novel mobile device user interface with integrated social networking servicesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2013.03.00471:9(919-932)Online publication date: 1-Sep-2013

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