Towards more effective encoders in pre-training for sequential recommendation

Pre-training emerges as a new learning paradigm in natural language processing and computer vision. It has also been introduced into sequential recommendation in several seminal studies for alleviating data sparsity issue. However, existing methods adopt the bidirectional transformer as the encoder which suffers from two drawbacks. One is insufficient intention modeling since the transformer architecture is suitable for extracting distributed consumption intention but cannot well catch users' concentrated and occasion consumption intentions. The other is information leakage caused by foreseeing the future item in advance during the bidirectional encoding process. To address these problems, we propose to construct more effective encoders in pre-training for sequential recommendation. Specifically, we first decouple the original bidirectional process in transformer structure into two unidirectional processes which can avoid the information leakage problem and capture the distributed consumption intention. We then employ the locality-aware convolutional neural networks (CNNs) with narrow receptive field for concentrated consumption modeling. We also introduce a random shuffle strategy to empower CNN with the ability of modeling the occasion consumption. Experiments on five datasets demonstrate that our method improves the performance of various types of downstream sequential recommendation models to a large extent, and it also generates the overall better performance than the state-of-the-art self-supervised pre-training methods.