Abstract:

Due to the large number of user groups and resources in the employment system for vocational college graduates, the weight of employment resources varies significantly, making it difficult to generate recommendation labels uniformly, resulting in the inability of the graduate employment system to complete on-demand resource recommendations. Therefore, a multi type resource on-demand recommendation algorithm is designed for the employment system of vocational college graduates. By extracting multidimensional features of user information from historical data and utilizing long short-term memory neural networks to fuse multiple sources of data, effective labels are extracted to establish a user label library for the graduate employment system, forming user profiles. Based on the general situation of user profiles, combined with the Ebbinghaus forgetting curve, the label matrix of multiple types of employment resources is evaluated, a content topic model is established, and spectral clustering algorithm is used for graph segmentation. Different weight values are assigned to different employment resources based on similarity, and normalization is implemented to generate secondary labels, completing the labeling process of employment resources. A regional preference base for graduates' employment is constructed, associating and matching user profiles with employment resource labels in designated geographical locations, and score the recommendation results through expert ranking weighting. Experiments are conducted on the above design, and the results show that the hit rate of the algorithm's recommended results is greater than 0. 9, indicating high accuracy.

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