论文部分内容阅读
An improved multi-task learning recommendation algorithm—fast two-stage multi-task recommendation model boosted feature selection (Fast TMRM) is proposed based on auto-encoders in this paper.Compared to previous work,Fast TMRM improves the convergence speed and accuracy of training.In addition,Fast TMRM builds on previous work to introduce the auto-encoder to encode the important feature combination vector.That is how it can be used for the training of multi-task learning,which helps to improve the training efficiency of the model by nearly 67％.Finally,the nearest neighbor search is used to restore important feature expression.