## 3 Proposed Job Recommendation Method
### Experimental Evaluation  
#### 3.3.1 Recommendation Method  
To rigorously assess the efficacy of the job recommendation approach proposed in this study, it underwent evaluation and comparison with existing recommendation methods. The description of the recommended method is provided as follows:  
TF-C [38]: This method employs cosine similarity to compute the resemblance between the text within a college graduate's resume and the content of the job posting. The system then produces a list of job recommendations based on similarity scores for college graduates to evaluate. The rating scale ranges from \(1\) (unsatisfactory) to \(5\) (satisfactory), with a total score of \(5\).  
MS [43]: In the screening stage, a semantic keyword-matching approach is used to evaluate the likeness between the content of the college graduate's resume and the text of the job posting. Subsequently, a list of job recommendations is generated based on similarity and submitted to college graduates for scoring.  
GERS [35]: Employment characteristics of graduates are analyzed using a two-dimensional matrix of graduate preferences. Subsequently, the system calculates the similarity between graduates to identify jobs chosen by neighboring graduates, generating a recommendation list.  
RSCF [44]: The similarity between graduates' resumes and job descriptions is integrated with a collaborative filtering algorithm based on graduates' similarity to calculate a list of job recommendations. The recommended jobs are then provided to college graduates for scoring.  
JRSR: Utilizing the job recommendation method introduced in this paper, the screening stage uses a semantic keyword iterative algorithm to compute the similarity between resume text and recruitment text. In the ranking stage, three dimensions--\"professional matching, salary matching, and social network\"--are used to generate a set of job recommendations using entropy aggregation. This collection is then returned to college graduates for scoring.  
To assess the impact of each dimension of the job recommendation method (JRSR) on the computational method's performance, this study disaggregates the system into its constituent components--semantic keyword matching, professional matching, salary matching, and social networking--and conducts an experimental evaluation of each. RSR omits the semantic keyword-matching method, relying solely on professional alignment, salary comparison, and social network inputs for its calculations. JSR excludes professional alignment, utilizing only semantic keyword matching, salary comparison, and social networks. JRR discards salary comparison, operating with semantic keyword matching, professional alignment, and social networks. In contrast, JRS disregards social networking elements, conducting analyses strictly with semantic keyword matching, professional alignment, and salary matching components. The aforementioned methods were individually applied to the dataset, which comprised 110 target graduates, 320 total graduates, and 280 work samples, to perform calculations, with the recommendation list set at a length of five.  
To evaluate the impact of graduates' historical employment information on recommendation performance, this study categorizes the employment data into two sets: one for logistics engineering and another for management and science work samples. Subsequently, the job recommendation method (JRSR) proposed in this study was applied separately to each work sample set, generating two sets of recommendation results. Finally, the performance of the job recommendation method is assessed and compared based on the evaluation results from both sets of work samples.