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Social network document ranking

Published: 21 June 2010 Publication History

Abstract

In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not meet the diverse interests of users, who may demand different documents even with the same queries. To satisfy this need for more personalized ranking, we propose a ranking framework. Social Network Document Rank (SNDocRank), that considers both document contents and the relationship between a searched and document owners in a social network. This method combined the traditional tf-idf ranking for document contents with out Multi-level Actor Similarity (MAS) algorithm to measure to what extent document owners and the searcher are structurally similar in a social network. We implemented our ranking method in simulated video social network based on data extracted from YouTube and tested its effectiveness on video search. The results show that compared with the traditional ranking method like tf-idfs the SNDocRank algorithm returns more relevant documents. More specifically, a searcher can get significantly better results be being in a larger social network, having more friends, and being associated with larger local communities in a social network.

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cover image ACM Conferences
JCDL '10: Proceedings of the 10th annual joint conference on Digital libraries
June 2010
424 pages
ISBN:9781450300858
DOI:10.1145/1816123
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: 21 June 2010

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Author Tags

  1. information retrieval
  2. multilevel actor similarity
  3. ranking
  4. social networks

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  • Research-article

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JCDL10
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JCDL10: Joint Conference on Digital Libraries
June 21 - 25, 2010
Queensland, Gold Coast, Australia

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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  • (2020)2SRM: Learning social signals for predicting relevant search resultsWeb Intelligence10.3233/WEB-20042618:1(15-33)Online publication date: 4-Mar-2020
  • (2020)An automatic learning for re-ranking in social information retrieval2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA10.1109/SMAP49528.2020.9248437(1-6)Online publication date: 29-Oct-2020
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  • (2019)Lurkers Versus Posters: Investigation of the Participation Behaviors in Online Learning CommunitiesEducational Networking10.1007/978-3-030-29973-6_8(269-298)Online publication date: 9-Nov-2019
  • (2017)Social Search Technique with the Consideration of User LocationProceedings of the 4th Multidisciplinary International Social Networks Conference10.1145/3092090.3092116(1-4)Online publication date: 17-Jul-2017
  • (2017)Advanced searching framework for open online educational video lecturesSocial Network Analysis and Mining10.1007/s13278-017-0452-37:1Online publication date: 26-Jul-2017
  • (2017)Leveraging social information for personalized searchSocial Network Analysis and Mining10.1007/s13278-017-0435-47:1Online publication date: 26-Apr-2017
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  • (2015)Ranking educational videos: The impact of social presence2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2015.7128895(342-350)Online publication date: May-2015
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