To address the problem of strong feature overfitting and weak feature underfitting problem when learning user representations using Embedding layer & Multi-Layer Perceptron （Embedding&MLP） paradigm for recommendation systems and the problem of learning user interests using Gated Recurrent Unit （GRU） without considering that the influence of current behaviors on users’ final interests diminishes over time， a Recruitment Recommendation Model based on Field Fusion and Time Weight （RecRec） was proposed. In RecRec， firstly， a new domain fusion layer was adopted to replace the traditional tandem layer， and the domain fusion layer showed a significantly superior performance on multi-domain features. Then， time weight was incorporated into GRU in the interest evolution layer， and a TimeStamp Gated Recurrent Unit （TSGRU） was proposed， by which made the user interests were learned more accurately. Ultimately， personalised recommendations were achieved by predicting the dial-up rate of users. Experimental results show that the Area Under Curve （AUC） of RecRec improves by 0.03 to 0.36 percentage points compared to YouTube Deep Neural Network （DNN）， Wide&Deep， Auxiliary LSTM-Attention Matrix Factorization （ALAMF） and Long-term & Short-term Sequential Self-Attention Network （LSSSAN）， indicating that RecRec can effectively learn user representations and user interests.