It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization problems, interactive evolutionary algorithms, which can make use of human preferences, are highly desirable. However, due to the lack of effective encoding methods, interactive evolutionary algorithms have been limited to numerically encoded optimization problems. In practice, however, linguistic terms (words) are the most natural expression of human preferences, and they are also commonly used to describe items in personalized search or E-commerce; therefore, language models better suit encoding, and the optimization of personalized search is converted into a dynamic document matching problem. To optimize word-described personalized search, we propose a novel interactive estimation of distribution algorithm. This algorithm combines a language model-based encoding approach, a Dirichlet-Multinomial compound distribution-based preference expression, and a Bayesian inference mechanism. The proposed algorithm is applied to two personalized search cases to demonstrate the capability of the algorithm in ensuring a more efficient and accurate search with less user fatigue.

由于很难在数学上描述定性问题，因此使用分析目标函数来优化个性化搜索非常困难，甚至并非不可能。为了解决这种优化问题，非常需要可以利用人类偏好的交互式进化算法。但是，由于缺乏有效的编码方法，交互式进化算法已限于数字编码的优化问题。然而，实际上，语言术语（单词）是人类偏好的最自然表达，它们也通常用于描述个性化搜索或电子商务中的项目。因此，语言模型更适合编码，并且个性化搜索的优化被转换为动态文档匹配问题。为了优化单词描述的个性化搜索，我们提出了一种新颖的交互式分配算法估计。该算法结合了基于语言模型的编码方法，基于Dirichlet多项式复合分布的偏好表达和贝叶斯推理机制。将该算法应用于两个个性化搜索案例，以证明该算法在确保更有效，更准确的搜索的同时减少用户疲劳的能力。