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Recommending topics for self-descriptions in online user profiles

Published: 23 October 2008 Publication History

Abstract

Traditional social networking sites allow users to enter responses to a set of predefined fields when populating their personal profiles. In the system discussed in this work, freeform 'About You' entries allow users to craft their own questions / topics. We found that this kind of flexibility often leads to low content contributions and infrequent updates. The 'About You' recommender system described in this paper differs from many recommender systems in that it recommends content for users to create, rather than consume. We present empirical data from an experiment with 2,000 users of a social networking site during a one month period. Our findings suggest that users who receive recommendations create more entries and update them more over time. Further, using articulated social network information for recommendations performed better than content-based matching.

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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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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Publication History

Published: 23 October 2008

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Author Tags

  1. impression formation
  2. recommendation
  3. self description
  4. social networking
  5. user profiles

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2017)A user intention modeling algorithm for friend recommendation2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(10.1109/ICBDA.2017.8078745(789-795)Online publication date: Mar-2017
  • (2017)Research on individualized recommendation algorithm for Tibetan micro-blog2017 3rd IEEE International Conference on Computer and Communications (ICCC)10.1109/CompComm.2017.8323003(2589-2593)Online publication date: Dec-2017
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