基于最大团的条件偏好挖掘

针对在数据库的个性化查询中条件约束（或上下文约束）没有被充分考虑的问题，首先提出了条件约束模型i⁺?i^-|X，它表示在上下文X的约束下，相对于i^-，用户更偏好i⁺。在此模型的基础上，采用最大团（MaxClique）关联规则算法挖掘获得用户偏好；随后又提出了条件偏好挖掘（CPM）算法，该算法结合上下文用于挖掘偏好规则，从而得出用户的偏好。实验结果表明，基于CPM算法的偏好挖掘模型具有较强的偏好表达能力，将CPM算法与基于Apriori的算法以及CONTENUM算法进行了实验对比，实验的主要参数为最小支持度、最小可信度、数据规模等，实验结果进一步表明所提出的CPM算法可明显提高用户偏好规则的产生效率。

Conditional preference mining based on MaxClique

In order to solve the problem that conditional constraints (context constraints) for personalized queries in database were not fully considered, a constraint model was proposed where the context i^+?i-|X means that the user prefers i⁺ than i^- based on the constraint of context X. Association rules mining algorithm based on MaxClique was used to obtain user preferences, and Conditional Preference Mining (CPM) algorithm combined with context obtained preference rules was proposed to obtain user preferences. The experimental results show that the context preference mining model has strong preference expression ability. At the same time, under the different parameters of minimum support, minimum confidence and data scale, the experimental results of preferences mining algorithm of CPM compared with Apriori algorithm and CONTENUM algorithm show that the proposed CPM algorithm can obviously improve the generation efficiency of user preferences.