Hierarchical Multi-Task Learning Framework for Session-based
  Recommendations
Sejoon Oh Walid Shalaby Amir Afsharinejad Xiquan Cui
While session-based recommender systems (SBRSs) have shown superior
recommendation performance, multi-task learning (MTL) has been adopted by SBRSs
to enhance their prediction accuracy and generalizability further. Hierarchical
MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds
outputs from auxiliary tasks to main tasks. This hierarchy leads to richer
input features for main tasks and higher interpretability of predictions,
compared to existing MTL frameworks. However, the H-MTL framework has not been
investigated in SBRSs yet. In this paper, we propose HierSRec which
incorporates the H-MTL architecture into SBRSs. HierSRec encodes a given
session with a metadata-aware Transformer and performs next-category prediction
(i.e., auxiliary task) with the session encoding. Next, HierSRec conducts
next-item prediction (i.e., main task) with the category prediction result and
session encoding. For scalable inference, HierSRec creates a compact set of
candidate items (e.g., 4% of total items) per test example using the category
prediction. Experiments show that HierSRec outperforms existing SBRSs as per
next-item prediction accuracy on two session-based recommendation datasets. The
accuracy of HierSRec measured with the carefully-curated candidate items aligns
with the accuracy of HierSRec calculated with all items, which validates the
usefulness of our candidate generation scheme via H-MTL.