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Nearest neighbour based social recommendation using heat diffusion

Published: 22 August 2013 Publication History

Abstract

Growth of the internet has lead to information overload. Recommender systems filter this vast amount of information and outputs useful information. They are traditionally based on users rating of items. But those systems were not useful for coldstart users i.e. users who have made very few purchases. So social recommenders began to evolve which makes use of social networks to improve recommendations. In this paper we propose a nearest neighbour based top N recommendation technique using social network. We model the data sources as graph and use heat diffusion process for generating recommendations. The experimental evaluation on the epinions dataset shows that our approach outperforms the approach that combines user based collaborative filtering approach and trust based approach.

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  • (2021)Time-sensitive Positive Influence Maximization in signed social networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2021.126353584(126353)Online publication date: Dec-2021
  • (2016)Research Fronts of Robust Social Recommendation2016 9th International Conference on Service Science (ICSS)10.1109/ICSS.2016.24(128-133)Online publication date: Oct-2016
  • (2016)Robust Social Recommendation Techniques: A ReviewSocially Aware Organisations and Technologies. Impact and Challenges10.1007/978-3-319-42102-5_6(53-58)Online publication date: 22-Jul-2016

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      cover image ACM Other conferences
      Compute '13: Proceedings of the 6th ACM India Computing Convention
      August 2013
      196 pages
      ISBN:9781450325455
      DOI:10.1145/2522548
      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: 22 August 2013

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      Author Tags

      1. collaborative filtering
      2. heat diffusion
      3. recommendation
      4. top N
      5. trust

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      Compute '13
      Compute '13: The 6th ACM India Computing Convention
      August 22 - 25, 2013
      Tamil Nadu, Vellore, India

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      Compute '13 Paper Acceptance Rate 24 of 96 submissions, 25%;
      Overall Acceptance Rate 114 of 622 submissions, 18%

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      View all
      • (2021)Time-sensitive Positive Influence Maximization in signed social networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2021.126353584(126353)Online publication date: Dec-2021
      • (2016)Research Fronts of Robust Social Recommendation2016 9th International Conference on Service Science (ICSS)10.1109/ICSS.2016.24(128-133)Online publication date: Oct-2016
      • (2016)Robust Social Recommendation Techniques: A ReviewSocially Aware Organisations and Technologies. Impact and Challenges10.1007/978-3-319-42102-5_6(53-58)Online publication date: 22-Jul-2016

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