发表刊物：IEEE Transactions on Emerging Topics in Computational Intelligence (JCR一区，影响因子4.851)
摘要：As an emerging topic on preference learning, aiming at deducting the linear order of alternatives from the partial ranking, preference completion is to complete the preference of the target agent to form a linear order from the preferences of other agents under certain complex requirements. In order to improve the effectiveness and efficiency of preference completion in Big Data environments, firstly the preference graph is introduced to represent the collective preference of the agents over the alternatives with a certain consensus algorithm following the preference of the target agent. This preference graph can preserve rich information between agents. In addition, with the introduction of fuzzy ranking, it can illustrate the fuzziness of the target agent that can include several ranking options of the target agent over alternatives. Then, the satisfied preference can be matched from the preference graph with the fuzzy ranking requested by the target agent via isomorphism-based graph pattern matching. With the matched preference, the preference of the target agent can be completed. If the completed preference is not satisfied, the target agent can modify the fuzzy ranking, process the graph pattern rematching and complete the preference again. The experimental results show that with several real datasets the effectiveness and efficiency of the fuzzy ranking-based preference completion via graph pattern matching can be validated.
合写作者：刘盼,张赞,吴信东
第一作者：李磊
论文类型：期刊论文
通讯作者：卜晨阳
卷号：8
期号：2
页面范围：2009-2021
是否译文：否
发表时间：2024-04-01
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/document/10432951