## 3 Proposed Job Recommendation Method
### Screening Phase
#### 3.1.2 The Semantic Keyword Iterative Algorithm  
[PERSON] et al. [38] pointed out that traditional similarity calculation methods, such as cosine similarity, suffer from low utility when dealing with sparse data. Traditional methods rely solely on word frequency to calculate text similarity, making them susceptible to discarding keywords with low frequency, thus resulting in reduced calculation accuracy [38; 39]. In light of the sparse data and lack of historical information for college graduates as new users of job recommendation systems, this study uses a semantic keyword iterative algorithm. This algorithm leverages a keyword correlation matrix to calculate text similarity, thereby countering the accuracy issues associated with low-frequency keywords. This approach enhances the accuracy of the job recommendation system, enabling it to provide more reliable recommendations to college graduates, despite their limited historical data. The keyword correlation matrix is calculated using Equations (1)-(5). First, the TFIDF algorithm was used to calculate the 'keyword-text' matrix, referred to as the KD matrix. KD is an \(\text{n}_{\text{k}}\times\text{n}_{\text{d}}\) matrix, where \(\text{n}_{\text{k}}\) represents the number of keywords, \(\text{n}_{\text{d}}\) represents the number of texts, and KD represents the number of times the keyword appears in the text; for example, KD\({}_{\text{im}}\) represents the number of times the keyword \(\text{k}_{\text{i}}\) appears in the text \(\text{D}_{\text{m}}\). The keyword correlation matrix is calculated based on the KD matrix. The specific calculation process is as follows:  
Step 1:  
\[sk^{0}(k_{m},\ k_{n})=\theta_{mm},\ sd^{0}(d_{m},\ d_{n})=\delta_{mn} \tag{1}\]  
The first step is to define the initial similarity value of the keyword \(\text{sk}^{0}(\text{k}_{\text{m}},\text{k}_{\text{n}})\) and the text \(\text{sd}^{0}(\text{d}_{\text{m}},\ \text{d}_{\text{n}})\). The principle of definition is that each keyword or text is the same as itself and has certain differences from other keywords and texts. \(\text{sk}^{0}(\text{k}_{\text{m}},\ \text{k}_{\text{n}})\) represents the initial similarity value between keywords \(\text{k}_{\text{m}}\) and \(\text{k}_{\text{n}}\), and \(\text{sd}^{0}(\text{d}_{\text{m}},\ \text{d}_{\text{n}})\) represents the initial similarity value between text \(\text{d}_{\text{m}}\) and \(\text{d}_{\text{n}}\). Step n:  
\[sk^{n}(k_{m},k_{n})=\frac{SKK^{n}(k_{m},k_{n})}{\sqrt{SKK^{n}(k_{m},k_{m})} \cdot\sqrt{SKK^{n}(k_{n},k_{n})}} \tag{2}\]  
\[sd^{n}(d_{m},d_{n})=\frac{SDD^{n}(d_{m},d_{n})}{\sqrt{SDD^{n}(d_{m},d_{m})} \cdot\sqrt{SDD^{n}(d_{n},d_{n})}} \tag{3}\]  
Where  
\[SKK^{n}(k_{m},k_{n})=\sum_{i,j=1}^{n_{\text{d}}}KD_{mi}\cdot\varphi_{ij}\cdot sd ^{n-1}\big{(}d_{i},d_{j}\big{)}\cdot KD_{nj} \tag{4}\]  
\[SDD^{n}(d_{m},d_{n})=\sum_{i,j=1}^{n_{\text{k}}}KD_{im}\cdot\varphi_{ij}\cdot sk ^{n-1}\big{(}k_{i},k_{j}\big{)}\cdot KD_{jn} \tag{5}\]  
The calculated value of \(\text{SKK}^{n}(\text{k}_{\text{m}},\text{k}_{\text{n}})\) in step n must be incorporated into the text similarity calculation of \(\text{sd}^{n-1}\big{(}\text{d}_{i},\text{d}_{\text{j}}\big{)}\) in step \(\text{n}\). The calculated value of \(\text{SDD}^{n}(\text{d}_{\text{m}},\text{d}_{\text{n}})\) in step n must be incorporated into the keyword similarity calculation of \(\text{sk}^{n-1}\big{(}\text{k}_{\text{i}},\text{k}_{\text{j}}\big{)}\) in step \(\text{(n-1)}\), and the iterative calculation is carried out in turn. KD\({}_{\text{mi}}\), KD\({}_{\text{mj}}\), KD\({}_{\text{im}}\), and KD\({}_{\text{jn}}\) in Equations (4) and (5) are elements of the KD matrix. \(\varphi_{\text{ij}}\) is the mutual reinforcement factor. When keywords \(\text{k}_{\text{i}}\) and \(\text{k}_{\text{j}}\) represent the same document, the mutual reinforcement factor \(\varphi_{\text{ij}}=1\). In the other cases, the value range of \(\varphi_{\text{ij}}\) is (0,1). The value of \(\varphi_{\text{ij}}\) was experimentally obtained.