  Session-based recommender systems (SBRSs) have shown superior performance
over conventional methods. However, they show limited scalability on
large-scale industrial datasets since most models learn one embedding per item.
This leads to a large memory requirement (of storing one vector per item) and
poor performance on sparse sessions with cold-start or unpopular items. Using
one public and one large industrial dataset, we experimentally show that
state-of-the-art SBRSs have low performance on sparse sessions with sparse
items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for
session-based recommendations. Our proposed method learns a transformation
function from item metadata to embeddings, and is thus, item-ID free (i.e.,
does not need to learn one embedding per item). It integrates item metadata to
learn shared representations of diverse item attributes. During inference, new
or unpopular items will be assigned identical representations for the
attributes they share with items previously observed during training, and thus
will have similar representations with those items, enabling recommendations of
even cold-start and sparse items. Additionally, M2TRec is trained in a
multi-task setting to predict the next item in the session along with its
primary category and subcategories. Our multi-task strategy makes the model
converge faster and significantly improves the overall performance.
Experimental results show significant performance gains using our proposed
approach on sparse items on the two datasets.
