TY - GEN
T1 - Roundtable gossip algorithm
T2 - 18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018
AU - Liu, Mengdi
AU - Xu, Guangquan
AU - Zhang, Jun
AU - Shankaran, Rajan
AU - Luo, Gang
AU - Zheng, Xi
AU - Zhang, Zonghua
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Cold Start (CS) and sparse evaluation problems dramatically degrade recommendation performance in large-scale recommendation systems such as Taobao and eBay. We name this degradation as the sparse trust problem, which will cause the decrease of the recommendation accuracy rate. To address this problem we propose a novel sparse trust mining method, which is based on the Roundtable Gossip Algorithm (RGA). First, we define the relevant representation of sparse trust, which provides a research idea to solve the problem of sparse evidence in the large-scale recommendation system. Based on which the RGA is proposed for mining latent sparse trust relationships between entities in large-scale recommendation systems. Second, we propose an efficient and simple anti-sparsification method, which overcomes the disadvantages of random trust relationship propagation and Grade Inflation caused by different users have different standard for item rating. Finally, the experimental results show that our method can effectively mine new trust relationships and mitigate the sparse trust problem.
AB - Cold Start (CS) and sparse evaluation problems dramatically degrade recommendation performance in large-scale recommendation systems such as Taobao and eBay. We name this degradation as the sparse trust problem, which will cause the decrease of the recommendation accuracy rate. To address this problem we propose a novel sparse trust mining method, which is based on the Roundtable Gossip Algorithm (RGA). First, we define the relevant representation of sparse trust, which provides a research idea to solve the problem of sparse evidence in the large-scale recommendation system. Based on which the RGA is proposed for mining latent sparse trust relationships between entities in large-scale recommendation systems. Second, we propose an efficient and simple anti-sparsification method, which overcomes the disadvantages of random trust relationship propagation and Grade Inflation caused by different users have different standard for item rating. Finally, the experimental results show that our method can effectively mine new trust relationships and mitigate the sparse trust problem.
KW - Anti-sparsification
KW - Recommendation system
KW - Sparse trust relationship
UR - http://www.scopus.com/inward/record.url?scp=85058644251&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05063-4_37
DO - 10.1007/978-3-030-05063-4_37
M3 - Conference proceeding contribution
AN - SCOPUS:85058644251
SN - 9783030050627
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 495
EP - 510
BT - Algorithms and Architectures for Parallel Processing
A2 - Vaidya, Jaideep
A2 - Li, Jin
PB - Springer-VDI-Verlag GmbH & Co. KG
CY - Switzerland
Y2 - 15 November 2018 through 17 November 2018
ER -