融合隐语义模型与门控循环单元的推荐算法

Recommendation Algorithm Combining Latent Factor Model and Gated Recurrent Unit

在传统的推荐算法中,往往缺乏对用户长短期兴趣偏好问题的考虑,而随着深度学习在推荐算法中应用的不断深入,这一问题能够得到很好的解决.本文针对该问题提出一种融合隐语义模型与门控循环单元的长短期推荐算法(recommendation algorithm based on long short-term,RA_LST),以实现对用户长短期偏好的分别捕捉,有效解决了因用户兴趣随时间变化而导致推荐效果下降的问题.最终的实验结果表明,本文提出的算法在不同的数据集上都表现出了推荐准确性的提升.

In traditional recommendation algorithms,there is often a lack of consideration of users’long short-term interest preferences.However,with the deepening of the application of deep learning in recommendation algorithms,this problem can be solved well.In response to the problem,this study proposes a recommendation algorithm based on long short-term interest preferences(RA_LST),which integrates a latent factor model and a gated recurrent unit.It can capture users’long short-term preferences respectively and thus effectively solves the problem that the recommendation effect decreases due to users’interest changing with time.The final experimental results show that the proposed algorithm improves the recommendation accuracy on different data sets.