Repeated Padding for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Xingwei Wang, Jianzhe Zhao
Abstract
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batch-based training needs to ensure that the sequences in each batch have the same length. The special value 0 is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has never been explored in the recommendation field: Can we utilize this idle input space by padding other content to improve model performance and training efficiency further?