Behavioral Feature Boosting via Substitute Relationships for E-commerce Search
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908835844718592 |
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| author | Dong, Chaosheng Momma, Michinari Wang, Yijia Gao, Yan Sun, Yi |
| author_facet | Dong, Chaosheng Momma, Michinari Wang, Yijia Gao, Yan Sun, Yi |
| contents | On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address this, we propose a simple yet effective behavior feature boosting method that leverages substitute relationships among products (BFS). BFS identifies substitutes-products that satisfy similar user needs-and aggregates their behavioral signals (e.g., clicks, add-to-carts, purchases, and ratings) to provide a warm start for new items. Incorporating these enriched signals into ranking models mitigates cold-start effects and improves relevance and competitiveness. Experiments on a large E-commerce platform, both offline and online, show that BFS significantly improves search relevance and product discovery for cold-start products. BFS is scalable and practical, improving user experience while increasing exposure for newly launched items in E-commerce search. The BFS-enhanced ranking model has been launched in production and has served customers since 2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14502 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Behavioral Feature Boosting via Substitute Relationships for E-commerce Search Dong, Chaosheng Momma, Michinari Wang, Yijia Gao, Yan Sun, Yi Information Retrieval On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address this, we propose a simple yet effective behavior feature boosting method that leverages substitute relationships among products (BFS). BFS identifies substitutes-products that satisfy similar user needs-and aggregates their behavioral signals (e.g., clicks, add-to-carts, purchases, and ratings) to provide a warm start for new items. Incorporating these enriched signals into ranking models mitigates cold-start effects and improves relevance and competitiveness. Experiments on a large E-commerce platform, both offline and online, show that BFS significantly improves search relevance and product discovery for cold-start products. BFS is scalable and practical, improving user experience while increasing exposure for newly launched items in E-commerce search. The BFS-enhanced ranking model has been launched in production and has served customers since 2025. |
| title | Behavioral Feature Boosting via Substitute Relationships for E-commerce Search |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.14502 |