A Simplified Bert Filter Denoising Model for Sequence Recommendation
Yunyang Xie, Nan Wang, Xin Xu, Yingli Zhong · 2024
In recent years, some deep neural network models like Recurrent Neural Network (RNN), transformer and Bert have been applied to sequence recommendation. It aims to obtain dynamic preference features from recorded user behavior data, so as to achieve ac- curate recommendations. However, the user's interaction history inevitably contains a lot of noise information, which is easy to mislead the training of the model. In order to address this issue, we propose a novel simplified Bert filter denoising sequence recommendation model (LightBertR). LightBertR adds a filter layer Bertf with a learnable filter to Bert, which adaptively attenuates noise information by converting time domain and frequency domain information to each other. The incorporation of the Bert filter layer facilitates the model's capacity to discern the intrinsic characteristics of the denoised interaction sequence, thereby enhancing the efficacy of the sequence recommendation model. Furthermore, LightBertR reduces the number of transformer layers in Bert model, markedly diminishes the training parameters of the model, and expedites model training. Extensive experiments on two real datasets demonstrate that LightBertR outperforms other Bert-based methods and numerous baseline methods in the recommendation field.