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 -