Talent Recommendation and Matching System Based on Collaborative Filtering
Qiaosong Jing · 2024
Aiming at the problems in the field of talent recommendation, this article puts forward a new talent recommendation algorithm, and verifies its effectiveness and performance advantages through experiments. The algorithm combines collaborative filtering (CF) technology and feature vector representation method, aiming at accurately locating the information needed by users from the huge talent database. Compared with the traditional methods, this algorithm shows significant improvement in prediction accuracy and recommendation coverage. In the aspect of experimental design, we choose real data sets and compare them with the traditional CF algorithm. By evaluating the MAE and recall rate, the excellent performance of this algorithm in reducing error and improving recall rate is verified. In addition, we also discuss the influence of different parameter settings on the performance of the algorithm, which provides guidance for practical application. The research results of this article not only bring new ideas and methods to the field of talent recommendation, but also provide valuable reference for the research and practice in related fields. Future work will further improve the algorithm model and expand its application scenarios to meet a wider range of user needs.