IU4Rec: Interest Unit-Based Product Organization and Recommendation for E-Commerce Platform

• URL: http://arxiv.org/abs/2502.07658v1
• Date: Tue, 11 Feb 2025 15:46:28 GMT
• Title: IU4Rec: Interest Unit-Based Product Organization and Recommendation for E-Commerce Platform
• Authors: Wenhao Wu, Xiaojie Li, Lin Wang, Jialiang Zhou, Di Wu, Qinye Xie, Qingheng Zhang, Yin Zhang, Shuguang Han, Fei Huang, Junfeng Chen,
• Abstract summary: Most items on Xianyu posted from individual sellers often have limited stock available for distribution, and once the product is sold, it's no longer available for distribution. We introduce textbfIU4Rec, an textbfInterest textbfUnit-based two-stage textbfRecommendation system framework. In the first stage, the focus is on recommend these Interest Units, capturing broad-level interests. In the second stage, it guides users to find the best option among similar products within the selected
• Score: 43.562618054958925
• License:
• Abstract: Most recommendation systems typically follow a product-based paradigm utilizing user-product interactions to identify the most engaging items for users. However, this product-based paradigm has notable drawbacks for Xianyu~\footnote{Xianyu is China's largest online C2C e-commerce platform where a large portion of the product are post by individual sellers}. Most of the product on Xianyu posted from individual sellers often have limited stock available for distribution, and once the product is sold, it's no longer available for distribution. This result in most items distributed product on Xianyu having relatively few interactions, affecting the effectiveness of traditional recommendation depending on accumulating user-item interactions. To address these issues, we introduce \textbf{IU4Rec}, an \textbf{I}nterest \textbf{U}nit-based two-stage \textbf{Rec}ommendation system framework. We first group products into clusters based on attributes such as category, image, and semantics. These IUs are then integrated into the Recommendation system, delivering both product and technological innovations. IU4Rec begins by grouping products into clusters based on attributes such as category, image, and semantics, forming Interest Units (IUs). Then we redesign the recommendation process into two stages. In the first stage, the focus is on recommend these Interest Units, capturing broad-level interests. In the second stage, it guides users to find the best option among similar products within the selected Interest Unit. User-IU interactions are incorporated into our ranking models, offering the advantage of more persistent IU behaviors compared to item-specific interactions. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed IU-centric recommendation approach.

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