利用混合Plackett-Luce模型的不完整序数偏好预测

Incomplete ordinal preference prediction using mixture of Plackett-Luce models

聚合不同用户的偏好时,基于序数偏好可以解决不同用户评价准则不一致问题。但用户因为候选项目过多、沟通成本高等原因不能提供完整序数偏好,影响了在线服务信誉度量、群体决策等场景中聚合结果的可靠性和准确性,而现有的预测方法未充分考虑用户群体偏好分布的多样性。针对这一问题,提出一种利用混合Plackett-Luce(PL)模型的不完整序数偏好预测(MixPLPP)方法。首先基于用户现有偏好采样完整拓展排序,其次使用采样的完整排序学习混合PL模型,再次设计基于后验概率最大化的模型选择策略为用户选择模型,最后利用所选模型预测用户完整偏好。在公开数据集Movielens上的实验结果表明,所提方法的预测准确率和Kendall秩相关系数(Kendall CC),相较于向量相似度排序(VSRank)算法提升了5.0%和9.2%;相较于基于确定性的偏好补全(CPC)提升了1.5%和3.5%;相较于BayesMallows-4提升了0.9%和2.2%。实验结果验证了所提方法具有良好的预测能力,在多个数据集上的预测效果都更好。

When aggregating the preferences of different users,the problem of inconsistent evaluation criteria among users can be solved based on ordinal preferences.However,users are unable to provide complete ordinal preferences due to the large number of candidate programs and high communication costs,which affects the reliability and accuracy of aggregation results in scenarios such as online service reputation measurement and group decision making.Therefore,there is a need to predict users’complete ordinal preferences,but existing prediction methods do not fully consider the diversity of user group preference distribution.To address this problem,a Mixture of Plackett-Luce(PL)Preference Prediction for incomplete ordinal preference(MixPLPP)was proposed.First,the linear extensions were sampled based on the user’s existing preferences.Then,a mixture of PL models was learned using the sampled linear extensions.Next,a model selection strategy based on maximization of posterior probability was designed to select a model for the user.Finally,the user’s complete preferences were predicted based on the selected model.The experimental results on the public dataset Movielens show that the proposed method improves the prediction accuracy and Kendall rank Correlation Coefficient(Kendall CC)by 5.0%and 9.2%compared to VSRank(Vector Similarity Rank)algorithm;1.5%and 3.5%compared to Certainty-based Preference Completion(CPC);0.9%and 2.2%compared to BayesMallows-4.The experimental results verify that the proposed method has good prediction ability and shows better prediction effect on multiple datasets and multiple measurements.