Sequential Recommendation with Bidirectional Chronological Augmentation of Transformer

Sequential recommendation can capture user chronological preferences from their historical behaviors, yet the learning of short sequences is still an open challenge. Recently, data augmentation with pseudo-prior items generated by transformers has drawn considerable attention in improving recommendation in short sequences and addressing the cold-start problem. These methods typically generate pseudo-prior items sequentially in reverse chronological order (i.e., from the future to the past) to obtain longer sequences for subsequent learning. However, the performance can still degrade for very short sequences than for longer ones. In fact, reverse sequential augmentation does not explicitly take into account the forward direction, and so the underlying temporal correlations may not be fully preserved in terms of conditional probabilities. In this paper, we propose a Bidirectional Chronological Augmentation of Transformer (BiCAT) that uses a forward learning constraint in the reverse generative process to capture contextual information more effectively. The forward constraint serves as a bridge between reverse data augmentation and forward recommendation. It can also be used as pretraining to facilitate subsequent learning. Extensive experiments on two public datasets with detailed comparisons to multiple baseline models demonstrate the effectiveness of our method, especially for very short sequences (3 or fewer items).