TY - GEN
T1 - MultiRank
T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
AU - NG, Kwok Po
AU - Li, Xutao
AU - Ye, Yunming
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.
AB - The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.
KW - Multi-relational data
KW - Ranking
KW - Rectangular tensors
KW - Stationary probability distribution
KW - Transition probability tensors
UR - http://www.scopus.com/inward/record.url?scp=80052650975&partnerID=8YFLogxK
U2 - 10.1145/2020408.2020594
DO - 10.1145/2020408.2020594
M3 - Conference contribution
AN - SCOPUS:80052650975
SN - 9781450308137
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1217
EP - 1225
BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Y2 - 21 August 2011 through 24 August 2011
ER -