Resumen
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.