  The goal of session-based recommendation in E-commerce is to predict the next
item that an anonymous user will purchase based on the browsing and purchase
history. However, constructing global or local transition graphs to supplement
session data can lead to noisy correlations and user intent vanishing. In this
work, we propose the Frequent Attribute Pattern Augmented Transformer (FAPAT)
that characterizes user intents by building attribute transition graphs and
matching attribute patterns. Specifically, the frequent and compact attribute
patterns are served as memory to augment session representations, followed by a
gate and a transformer block to fuse the whole session information. Through
extensive experiments on two public benchmarks and 100 million industrial data
in three domains, we demonstrate that FAPAT consistently outperforms
state-of-the-art methods by an average of 4.5% across various evaluation
metrics (Hits, NDCG, MRR). Besides evaluating the next-item prediction, we
estimate the models' capabilities to capture user intents via predicting items'
attributes and period-item recommendations.
