Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance. However, collecting users' explicit feedback is costly. In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval systems. Specifically, we adopt a two-tower neural net architecture to model query-item relevance given both collaborative and content information. By introducing auxiliary tasks trained with much richer implicit user feedback data, we improve the quality and resolution for the learned representations of queries and items. Applying these learned representations to an industrial retrieval system has delivered significant improvements.
Ruoxi Wang (Google AI)
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2019 : Poster Session »
Ivana Balazevic · Minae Kwon · Benjamin Lengerich · Amir Asiaee · Alex Lambert · Wenyu Chen · Yiming Ding · Carlos Florensa · Joseph E Gaudio · Yesmina Jaafra · Boli Fang · Ruoxi Wang · Tian Li · SWAMINATHAN GURUMURTHY · Andy Yan · Kubra Cilingir · Vithursan (Vithu) Thangarasa · Alexander Li · Ryan Lowe