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Distributed collaborative filtering for peer-to-peer file sharing systems

Published: 23 April 2006 Publication History

Abstract

Collaborative filtering requires a centralized rating database. However, within a peer-to-peer network such a centralized database is not readily available. In this paper, we propose a fully distributed collaborative filtering method that is self-organizing and operates in a distributed way. Similarity ranks between multimedia files (items) are calculated by log-based user profiles and are stored locally at these items in so-called buddy tables. This intuitively creates a semantic overlay to organize multimedia files. Based on this semantic overlay and the items that a user has downloaded previously (indicating the profile of the user), recommendations can be performed and the recommended items can be easily located. We have tested our distributed collaborative filtering approach and compared it to centralized collaborative filtering, showing that it has similar performance. It is therefore a promising technique to facilitate filtering for relevant multimedia data in P2P networks.

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  • (2019)Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems PerformanceInformation10.3390/info1005015510:5(155)Online publication date: 26-Apr-2019
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cover image ACM Conferences
SAC '06: Proceedings of the 2006 ACM symposium on Applied computing
April 2006
1967 pages
ISBN:1595931082
DOI:10.1145/1141277
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 April 2006

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Author Tags

  1. collaborative filtering
  2. peer-to-peer networks
  3. personalization
  4. recommendation

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Cited By

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  • (2021)Research on Elementary School Students’ Books Recommendation Algorithm Based on Words and Character Library2021 2nd International Conference on Artificial Intelligence and Information Systems10.1145/3469213.3470228(1-5)Online publication date: 28-May-2021
  • (2021)A Comparative Study of CF And NCF In Children's Book Recommender System2021 3rd International Workshop on Artificial Intelligence and Education (WAIE)10.1109/WAIE54146.2021.00017(43-47)Online publication date: Nov-2021
  • (2019)Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems PerformanceInformation10.3390/info1005015510:5(155)Online publication date: 26-Apr-2019
  • (2018)A Survey on the Scalability of Recommender Systems for Social NetworksSocial Networks Science: Design, Implementation, Security, and Challenges10.1007/978-3-319-90059-9_5(89-110)Online publication date: 19-Jun-2018
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  • (2016)"360° user profiling: past, future, and applications" by Aleksandr Farseev, Mohammad Akbari, Ivan Samborskii and Tat-Seng Chua with Martin Vesely as coordinatorACM SIGWEB Newsletter10.1145/2956573.29565772016:Summer(1-11)Online publication date: 6-Jul-2016
  • (2016)The 8th ACM Web Science Conference 2016ACM SIGWEB Newsletter10.1145/2956573.29565742016:Summer(1-7)Online publication date: 6-Jul-2016
  • (2016)Parallel and Distributed Collaborative FilteringACM Computing Surveys10.1145/295195249:2(1-41)Online publication date: 13-Aug-2016
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  • (2016)Subdivision exterior calculus for geometry processingACM Transactions on Graphics10.1145/2897824.292588035:4(1-11)Online publication date: 11-Jul-2016
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