Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest

• URL: http://arxiv.org/abs/2506.23060v1
• Date: Sun, 29 Jun 2025 02:14:21 GMT
• Title: Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest
• Authors: Zhibo Fan, Hongtao Lin, Haoyu Chen, Bowen Deng, Hedi Xia, Yuke Yan, James Li,
• Abstract summary: The retrieval stage plays a critical role in generating a high-recall set of candidate items.<n>Traditional two-tower models struggle in this regard due to limited user-item feature interaction.<n>We propose a novel multi-embedding retrieval framework designed to enhance user interest representation.
• Score: 9.904093205817247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial recommendation systems are typically composed of multiple stages, including retrieval, ranking, and blending. The retrieval stage plays a critical role in generating a high-recall set of candidate items that covers a wide range of diverse user interests. Effectively covering the diverse and long-tail user interests within this stage poses a significant challenge: traditional two-tower models struggle in this regard due to limited user-item feature interaction and often bias towards top use cases. To address these issues, we propose a novel multi-embedding retrieval framework designed to enhance user interest representation by generating multiple user embeddings conditioned on both implicit and explicit user interests. Implicit interests are captured from user history through a Differentiable Clustering Module (DCM), whereas explicit interests, such as topics that the user has followed, are modeled via Conditional Retrieval (CR). These methodologies represent a form of conditioned user representation learning that involves condition representation construction and associating the target item with the relevant conditions. Synergizing implicit and explicit user interests serves as a complementary approach to achieve more effective and comprehensive candidate retrieval as they benefit on different user segments and extract conditions from different but supplementary sources. Extensive experiments and A/B testing reveal significant improvements in user engagements and feed diversity metrics. Our proposed framework has been successfully deployed on Pinterest home feed.

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