MotiR: Motivation-aware Retrieval for Long-Tail Recommendation

In the retrieval stage of recommendation systems, two-tower models are widely adopted for their efficiency as a predominant paradigm.However, this method, which relies on collaborative filtering signals, exhibits limitations in modeling similarity for long-tail items.To address this issue, we propose a Motivationaware Retrieval for Long-Tail Recommendation, named MotiR.The purchase motivations generated by LLMs represent a condensed abstraction of items' intrinsic attributes.By effectively integrating them with traditional item features, this approach enables the two-tower model to capture semantic-level similarities among long-tail items.Furthermore, a gated network-based adaptive weighting mechanism dynamically adjusts representation weights: emphasizing semantic modeling for long-tail items while preserving collaborative signal advantages for popular items.Experimental results demonstrate 60.5% Hit@10 improvements over existing methods on Amazon Books.Industrial deployment in Taobao&Tmall Group 88VIP scenarios achieves over 4% CTR and CVR improvement, validating the effectiveness of our method.