Beyond Personalization and Anonymity: Towards a Group-Based Recommender System

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Beyond Personalization and Anonymity: Towards a Group-Based Recommender System. / Shang, Shang; Hui, Yuk; Hui, Pan et al.
Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Association for Computing Machinery, Inc, 2014. p. 266-273 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Shang, S, Hui, Y, Hui, P, Cuff, P & Kulkarni, S 2014, Beyond Personalization and Anonymity: Towards a Group-Based Recommender System. in Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, Inc, pp. 266-273, 29th Symposium On Applied Computing - SAC 2014 , Gyeongju, Korea, Republic of, 24.03.14. https://doi.org/10.1145/2554850.2554924

APA

Shang, S., Hui, Y., Hui, P., Cuff, P., & Kulkarni, S. (2014). Beyond Personalization and Anonymity: Towards a Group-Based Recommender System. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014 (pp. 266-273). (Proceedings of the ACM Symposium on Applied Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2554850.2554924

Vancouver

Shang S, Hui Y, Hui P, Cuff P, Kulkarni S. Beyond Personalization and Anonymity: Towards a Group-Based Recommender System. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Association for Computing Machinery, Inc. 2014. p. 266-273. (Proceedings of the ACM Symposium on Applied Computing). doi: 10.1145/2554850.2554924

Bibtex

@inbook{e631f806b35848179d2208b8dc66b14a,
title = "Beyond Personalization and Anonymity:: Towards a Group-Based Recommender System",
abstract = "Recommender systems have received considerable attention in recent years. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and pri- vacy in recommender systems naturally arise. In this pa- per, we present a privacy preserving recommendation frame- work based on groups. The main idea is to use groups as a natural middleware to preserve users{\textquoteright} privacy. A dis- tributed preference exchange algorithm is proposed to en- sure the anonymity of data, wherein the effective size of the anonymity set asymptotically approaches the group size with time. We construct a hybrid collaborative filtering model based on Markov random walks to provide recom- mendations and predictions to group members. Experimen- tal results on the MovieLens dataset show that our proposed methods outperform the baseline methods, L+ and Item- Rank, two state-of-the-art personalized recommendation al- gorithms, for both recommendation precision and hit rate despite the absence of personal preference information.",
keywords = "Digital media, Privacy, Recommendation System, Group-based social networks",
author = "Shang Shang and Yuk Hui and Pan Hui and Paul Cuff and Sanjeev Kulkarni",
year = "2014",
doi = "10.1145/2554850.2554924",
language = "English",
isbn = "978-1-4503-2469-4",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery, Inc",
pages = "266--273",
booktitle = "Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014",
address = "United States",
note = "29th Symposium On Applied Computing - SAC 2014 , SAC 2014 ; Conference date: 24-03-2014 Through 28-03-2014",
url = "https://www.sigapp.org/sac/sac2014/",

}

RIS

TY - CHAP

T1 - Beyond Personalization and Anonymity:

T2 - 29th Symposium On Applied Computing - SAC 2014

AU - Shang, Shang

AU - Hui, Yuk

AU - Hui, Pan

AU - Cuff, Paul

AU - Kulkarni, Sanjeev

N1 - Conference code: 29

PY - 2014

Y1 - 2014

N2 - Recommender systems have received considerable attention in recent years. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and pri- vacy in recommender systems naturally arise. In this pa- per, we present a privacy preserving recommendation frame- work based on groups. The main idea is to use groups as a natural middleware to preserve users’ privacy. A dis- tributed preference exchange algorithm is proposed to en- sure the anonymity of data, wherein the effective size of the anonymity set asymptotically approaches the group size with time. We construct a hybrid collaborative filtering model based on Markov random walks to provide recom- mendations and predictions to group members. Experimen- tal results on the MovieLens dataset show that our proposed methods outperform the baseline methods, L+ and Item- Rank, two state-of-the-art personalized recommendation al- gorithms, for both recommendation precision and hit rate despite the absence of personal preference information.

AB - Recommender systems have received considerable attention in recent years. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and pri- vacy in recommender systems naturally arise. In this pa- per, we present a privacy preserving recommendation frame- work based on groups. The main idea is to use groups as a natural middleware to preserve users’ privacy. A dis- tributed preference exchange algorithm is proposed to en- sure the anonymity of data, wherein the effective size of the anonymity set asymptotically approaches the group size with time. We construct a hybrid collaborative filtering model based on Markov random walks to provide recom- mendations and predictions to group members. Experimen- tal results on the MovieLens dataset show that our proposed methods outperform the baseline methods, L+ and Item- Rank, two state-of-the-art personalized recommendation al- gorithms, for both recommendation precision and hit rate despite the absence of personal preference information.

KW - Digital media

KW - Privacy

KW - Recommendation System

KW - Group-based social networks

UR - http://www.scopus.com/inward/record.url?scp=84905669758&partnerID=8YFLogxK

U2 - 10.1145/2554850.2554924

DO - 10.1145/2554850.2554924

M3 - Article in conference proceedings

SN - 978-1-4503-2469-4

T3 - Proceedings of the ACM Symposium on Applied Computing

SP - 266

EP - 273

BT - Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014

PB - Association for Computing Machinery, Inc

Y2 - 24 March 2014 through 28 March 2014

ER -

DOI

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