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Session-based recommender systems (SBRSs) have shown superior performanceover conventional methods. However, they show limited scalability onlarge-scale industrial datasets since most models learn one embedding per item.This leads to a large memory requirement (of storing one vector per item) andpoor performance on sparse sessions with cold-start or unpopular items. Usingone public and one large industrial dataset, we experimentally show thatstate-of-the-art SBRSs have low performance on sparse sessions with sparseitems. We propose M2TRec, a Metadata-aware Multi-task Transformer model forsession-based recommendations. Our proposed method learns a transformationfunction 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 tolearn shared representations of diverse item attributes. During inference, newor unpopular items will be assigned identical representations for theattributes they share with items previously observed during training, and thuswill have similar representations with those items, enabling recommendations ofeven cold-start and sparse items. Additionally, M2TRec is trained in amulti-task setting to predict the next item in the session along with itsprimary category and subcategories. Our multi-task strategy makes the modelconverge faster and significantly improves the overall performance.Experimental results show significant performance gains using our proposedapproach on sparse items on the two datasets.

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations