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Distributed collaborative filtering with domain specialization

Published: 19 October 2007 Publication History

Abstract

User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.

References

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cover image ACM Conferences
RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems
October 2007
222 pages
ISBN:9781595937308
DOI:10.1145/1297231
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: 19 October 2007

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Author Tags

  1. distributed collaborative filtering
  2. mediation of user modeling data
  3. recommender systems

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RecSys07
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RecSys07: ACM Conference on Recommender Systems
October 19 - 20, 2007
MN, Minneapolis, USA

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

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Cited By

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  • (2023)DA-DAN: A Dual Adversarial Domain Adaption Network for Unsupervised Non-overlapping Cross-domain RecommendationACM Transactions on Information Systems10.1145/361782542:2(1-27)Online publication date: 26-Aug-2023
  • (2023)Matrix Factorization-Based Unify Multiple Interactions for Cross-Domain Recommendation Services2023 RIVF International Conference on Computing and Communication Technologies (RIVF)10.1109/RIVF60135.2023.10471788(148-152)Online publication date: 23-Dec-2023
  • (2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
  • (2021)Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start ProblemResearch Anthology on Multi-Industry Uses of Genetic Programming and Algorithms10.4018/978-1-7998-8048-6.ch070(1492-1512)Online publication date: 2021
  • (2021)Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start ProblemResearch Anthology on Multi-Industry Uses of Genetic Programming and Algorithms10.4018/978-1-7998-8048-6.ch042(829-850)Online publication date: 2021
  • (2021)Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing EnvironmentResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch035(751-769)Online publication date: 2021
  • (2021)Trust and Distrust based Cross-domain Recommender SystemApplied Artificial Intelligence10.1080/08839514.2021.188129735:4(326-351)Online publication date: 12-Feb-2021
  • (2020)Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start ProblemInternational Journal of Swarm Intelligence Research10.4018/IJSIR.202004010411:2(62-79)Online publication date: Apr-2020
  • (2020)Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start ProblemHandbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems10.4018/978-1-7998-3222-5.ch010(195-216)Online publication date: 2020
  • (2020)Towards Cognitive Recommender SystemsAlgorithms10.3390/a1308017613:8(176)Online publication date: 22-Jul-2020
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