## 3 Methods  
The model proposed in this paper uses the calculated user's attention level for specific job positions and historical user ratings to predict the ratings of jobs that users have not encountered yet. The model involves two stages: (1) the acquisition and classification of user attention and the rating tensor construction phase corresponding to the user; (2) the user rating prediction and job recommendation phase based on tensor decomposition. Acquisition and Grading of Job-Seeking Users' Attention and Construction of User-Corresponding Score Tensors  
The resume delivery and recruitment process based on the online job recruitment platform can be summarized as follows: For \(m\) job-seeking users and \(n\) recruiting positions (recruitment items), suppose a job-seeking user \(u_{i}\) (\(1\leq I\leq m\)) is in the position of a job-seeking user \(v_{j}\) (\(1\leq j\leq n\)) and pages were browsed \(p\) times, which were browsed for \(t_{i,k}\) seconds (\(1\leq k\leq p\)) on a specific timestamp, and the recruitment item was delivered or not (the act of submitting a resume). After the recruiting unit receives the resume, the staff will browse all job candidate summaries and selectively click to view the detailed resumes of job seekers who are interested in further contact. Then, if the staff of the recruiting unit is satisfied with the job candidate, they will click to send an interview invitation from the detailed resume page of the user. At this time, the user can choose to accept the invitation or reject the invitation on his user control panel page. After that, the job-seeking units will adopt their own methods to interview the job-seekers, which are generally beyond the control of the system. Finally, the recruiting unit will archive the hired user profile to the \"hired\" status. In order to ensure that this process can form a closed-loop interaction, the user and the recruiting unit do not click on the operation for a certain period of time as rejections. For example, if the unit does not click on the user's resume details or interview invitation within a given period of time, it is considered as a prompt to reject the user's operation. From the perspective of job seekers, the final expression of this process is shown in Equation (1):  
\[r_{ijk}=f\Big{(}<u_{i},v_{j},t_{ijk}>\Big{)} \tag{1}\]  
The triplet \(<\!\!u_{i},v_{j},t_{ijk}\!\!>\) can be obtained through user actions. \(r_{ij,k}\) is the user's rating on a recruitment item defined in this paper, which is generated by the behavior of the recruitment process:  
\[r\in enunrate(action)=1,2,3,4,5 \tag{2}\]  
The correspondence between action and score is as follows:  
1. \(r_{ij,k}=1\): user \(u_{i}\) browsed the recruitment item \(v_{j}\) but did not post the resume, and the behavior sequence was \(<\)do not post the resume\(>\);
2. \(r_{ij,k}=2\): User \(u_{i}\) browses the recruitment item \(v_{j}\) and submits a resume, but the resume is ignored by the recruiting unit. The behavior sequence is \(<\)submit the resume, ignore the resume\(>\);
3. \(r_{ij,k}=3\): User \(u_{i}\) browses the recruitment item \(v_{j}\), submits a resume, and the resume is clicked by the recruiting unit to view details, but the recruiting unit does not issue an interview invitation. The behavior sequence is \(<\)submit resume, view detailed resume, no invite\(>\);
4. \(r_{ij,k}=4\): The user \(u_{i}\) browsed the recruitment item \(v_{j}\), submitted a resume, and the resume was clicked by the recruiting unit to view the details and send an interview invitation, but in the end, the submitted file was not classified as \"hired\" status; the behavior sequence is \(<\)send resume, view detailed resume, interview invitation, not hired\(>\);
5.