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Interactive recommendations in social endorsement networks

Published: 26 September 2010 Publication History

Abstract

An increasing number of social networking platforms are giving users the option to endorse entities that they find appealing, such as videos, photos, or even other users. We define this model as a Social Endorsement Network, visualized as a bipartite graph with edges (endorsements) from users to endorsed entities. In this work, we formalize the problem of interactive recommendations in social endorsement networks: given a query of tags and a social endorsement network, the problem is to recommend entities that match the query and also share a significant number of common endorsers. We propose an efficient search engine for the solution of the problem, able to produce high-quality and explainable recommendations. The entire framework is designed in a principled and efficient manner, making it ideal for large-scale systems. In a thorough experimental evaluation on real datasets, we illustrate the efficacy of our methods and provide some valuable insight on social endorsement networks.

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Cited By

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  • (2018)Microblog oriented interest extraction with both content and network structureIntelligent Data Analysis10.3233/IDA-17341422:3(515-532)Online publication date: 7-May-2018
  • (2018)Endorsement Recommendation Using Instagram Follower Profiling2018 6th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT.2018.8528724(470-475)Online publication date: May-2018
  • (2014)Personalized Recommender System on Whom to Follow in TwitterProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.84(326-333)Online publication date: 3-Dec-2014
  • Show More Cited By

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cover image ACM Conferences
RecSys '10: Proceedings of the fourth ACM conference on Recommender systems
September 2010
402 pages
ISBN:9781605589060
DOI:10.1145/1864708
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: 26 September 2010

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Author Tags

  1. recommendation systems
  2. social endorsement
  3. social networks
  4. twitter

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  • Research-article

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RecSys '10
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RecSys '10: Fourth ACM Conference on Recommender Systems
September 26 - 30, 2010
Barcelona, Spain

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

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Cited By

View all
  • (2018)Microblog oriented interest extraction with both content and network structureIntelligent Data Analysis10.3233/IDA-17341422:3(515-532)Online publication date: 7-May-2018
  • (2018)Endorsement Recommendation Using Instagram Follower Profiling2018 6th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT.2018.8528724(470-475)Online publication date: May-2018
  • (2014)Personalized Recommender System on Whom to Follow in TwitterProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.84(326-333)Online publication date: 3-Dec-2014
  • (2012)Degree distributions of evolving alphabetic bipartite networks and their projectionsTheoretical Computer Science10.1016/j.tcs.2012.08.007466(20-36)Online publication date: 1-Dec-2012
  • (2011)Exploiting endorsement information and social influence for item recommendationProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010084(1131-1132)Online publication date: 24-Jul-2011
  • (2011)Mining tags using social endorsement networksProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2009946(195-204)Online publication date: 24-Jul-2011

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