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Make new friends, but keep the old: recommending people on social networking sites

Published: 04 April 2009 Publication History

Abstract

This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.

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    cover image ACM Conferences
    CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2009
    2426 pages
    ISBN:9781605582467
    DOI:10.1145/1518701
    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: 04 April 2009

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    Author Tags

    1. friend
    2. recommender system
    3. social networking

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    CHI '09 Paper Acceptance Rate 277 of 1,130 submissions, 25%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    • (2024)Ant Colony Optimization for Link Prediction in Online Social Networks: A Swarm Intelligence ApproachProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676123(632-641)Online publication date: 8-Aug-2024
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    • (2023)Embedding Based Retrieval in Friend RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591848(3330-3334)Online publication date: 19-Jul-2023
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