###### Abstract  
In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users' attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of \"job seeker user-position-attention layered\". Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms. job recommendation system; tensor decomposition; multi-source heterogeneous data; user attention hierarchy +
Footnote †: journal: Applied sciences  
0000-0001-9946-4600-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-0000  
e-commerce and social media, job recruitment platforms have more serious data sparsity and cold-start problems, which will affect the performance and effectiveness of recommendations [13]. The data sparsity problem here is caused by the interaction between users and enterprises, and the number of projects with common behaviors between users is rare. In job recruitment platforms, the situation is aggravated because users have complex and diverse professional backgrounds that are long-term stable, and the fields of companies are independent of each other [14]. Therefore, obtaining more user behavior features from users' non-rating behaviors as the basis for user modeling has gained more and more attention [15]. In general, recommendation methods recommend by predicting user preferences. Therefore, a good recommendation method needs to understand each user's past preferences and the reasons behind their preferences as well as interact with all users' preferences differently [16; 17]. In the field of recruitment, the differences and similarities in browsing behavior among different users can serve as important information to support preference prediction [18]. For example, unlike traditional platforms such as e-commerce or online movies, the behavior of job seekers on online job recruitment platforms when submitting resumes does not necessarily result in recruitment by recruiting companies, and for job seekers, job requirements need to be studied carefully. Therefore, job seekers need to read the relevant recruitment materials before deciding whether to submit a resume. Based on this, an important behavioral hypothesis can be made: job seekers will read relevant information and materials more carefully for positions that are more in line with expectations. At present, in addition to the traditional collaborative filtering-based methods, some scholars use evaluation text [19], job applicant resumes [20], and user feature labels [13] to analyze information to enhance personalized job recommendations. However, this content-based method has data availability difficulties in the field of job recruitment: (1) When a user submits a resume, there is a two-way selection link between the recruiting unit and the user.