Beyond Personalization and Anonymity: Towards a Group-Based Recommender System
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Association for Computing Machinery, Inc, 2014. S. 266-273 (Proceedings of the ACM Symposium on Applied Computing).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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}
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 -