Article

Distributed collaborative filtering with domain specialization

Authors:

Shlomo Berkovsky,

Francesco RicciAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 33 - 40

https://doi.org/10.1145/1297231.1297238

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.

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Cited By

Guo LLiu HZhu LGuan WCheng Z(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
https://dl.acm.org/doi/10.1145/3617825
Nguyen LNguyen TPham HPhan T(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
https://doi.org/10.1109/RIVF60135.2023.10471788
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(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
https://dl.acm.org/doi/10.1145/3487331
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Index Terms

Distributed collaborative filtering with domain specialization
1. Information systems

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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

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 ACM.

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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Published: 19 October 2007

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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

Guo LLiu HZhu LGuan WCheng Z(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
https://dl.acm.org/doi/10.1145/3617825
Nguyen LNguyen TPham HPhan T(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
https://doi.org/10.1109/RIVF60135.2023.10471788
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(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
https://dl.acm.org/doi/10.1145/3487331
Sharon Moses J. Dhinesh Babu L.D. (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
https://doi.org/10.4018/978-1-7998-8048-6.ch070
J. SL. D. DSrinivasan SM. N(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
https://doi.org/10.4018/978-1-7998-8048-6.ch042
Tang XXu HTan YGong Y(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
https://doi.org/10.4018/978-1-7998-5339-8.ch035
Richa Bedi P(2021)Trust and Distrust based Cross-domain Recommender SystemApplied Artificial Intelligence10.1080/08839514.2021.188129735:4(326-351)Online publication date: 12-Feb-2021
https://doi.org/10.1080/08839514.2021.1881297
Sharon Moses J. Dhinesh Babu L.D. (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
https://doi.org/10.4018/IJSIR.2020040104
J. SL. D. DSrinivasan SM. N(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
https://doi.org/10.4018/978-1-7998-3222-5.ch010
Beheshti AYakhchi SMousaeirad SGhafari SGoluguri SEdrisi M(2020)Towards Cognitive Recommender SystemsAlgorithms10.3390/a1308017613:8(176)Online publication date: 22-Jul-2020
https://doi.org/10.3390/a13080176
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