“The critical process of personalized search is to reorder candidate documents of the current querybased on the user’s historical behavior sequence. There are many types of information containedin user historical information sequence, such as queries, documents, and clicks. Most existingpersonalized search approaches concatenate these types of information to get an overall userrepresentation, but they ignore the associations among them. We believe the associations ofdifferent information mentioned above are significant to personalized search. Based on a hierar-chical transformer as base architecture, we design three auxiliary tasks to capture the associationsof different information in user behavior sequence. Under the guidance of mutual information,we adjust the training loss, enabling our PSMIM model to better enhance the information rep-resentation in personalized search. Experimental results demonstrate that our proposed methodoutperforms some personalized search methods.”