TY - GEN
T1 - Group preference aggregation
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
AU - Zhao, Hongke
AU - Liu, Qi
AU - Ge, Yong
AU - Kong, Ruoyan
AU - Chen, Enhong
N1 - Funding Information:
This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the National Natural Science Foundation of China (Grant No. 61403358, 61572032, 71571093) and the National High Technology Research and Development Program of China (Grant No. 2014AA015203). Qi Liu gratefully acknowledges the support of the MOEMicrosoft Key Laboratory of USTC and the support of the CCF-Tencent Open Research Fund.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.
AB - Group-oriented services such as group recommendations aim to provide services for a group of users. For these applications, how to aggregate the preferences of different group members is the toughest yet most important problem. Inspired by game theory, in this paper, we propose to explore the idea of Nash equilibrium to simulate the selections of members in a group by a game process. Along this line, we first compute the preferences (group-dependent optimal selections) of each individual member in a given group scene, i.e., an equilibrium solution of this group, with the help of two pruning approaches. Then, to get the aggregated unitary preference of each group from all group members, we design a matrix factorization-based method which aggregates the preferences in latent space and estimates the final group preference in rating space. After obtaining the group preference, group-oriented services (e.g., group recommendation) can be directly provided. Finally, we construct extensive experiments on two real-world data sets from multiple aspects. The results clearly demonstrate the effectiveness of our method.
KW - Group Recommendation
KW - Nash Equilibrium
KW - Preference Aggregation
UR - http://www.scopus.com/inward/record.url?scp=85014559782&partnerID=8YFLogxK
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U2 - 10.1109/ICDM.2016.64
DO - 10.1109/ICDM.2016.64
M3 - Conference contribution
AN - SCOPUS:85014559782
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 679
EP - 688
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2016 through 15 December 2016
ER -