CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs

• URL: http://arxiv.org/abs/2410.11464v1
• Date: Tue, 15 Oct 2024 10:11:18 GMT
• Title: CoActionGraphRec: Sequential Multi-Interest Recommendations Using Co-Action Graphs
• Authors: Yi Sun, Yuri M. Brovman,
• Abstract summary: eBay's data sparsity exceeds other e-commerce sites by an order of magnitude. We propose a text based two-tower deep learning model (Item Tower and User Tower) utilizing co-action graph layers. For the Item Tower, we represent each item using its co-action items to capture collaborative signals in a co-action graph that is fully leveraged by the graph neural network component.
• Score: 4.031699584957737
• License:
• Abstract: There are unique challenges to developing item recommender systems for e-commerce platforms like eBay due to sparse data and diverse user interests. While rich user-item interactions are important, eBay's data sparsity exceeds other e-commerce sites by an order of magnitude. To address this challenge, we propose CoActionGraphRec (CAGR), a text based two-tower deep learning model (Item Tower and User Tower) utilizing co-action graph layers. In order to enhance user and item representations, a graph-based solution tailored to eBay's environment is utilized. For the Item Tower, we represent each item using its co-action items to capture collaborative signals in a co-action graph that is fully leveraged by the graph neural network component. For the User Tower, we build a fully connected graph of each user's behavior sequence, with edges encoding pairwise relationships. Furthermore, an explicit interaction module learns representations capturing behavior interactions. Extensive offline and online A/B test experiments demonstrate the effectiveness of our proposed approach and results show improved performance over state-of-the-art methods on key metrics.

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