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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint technical report describing AlleCompanion, a production recommendation system deployed at an e-commerce platform, which combines neural retrieval with category constraints and expert rules to surface complementary products. The authors report deployment at scale and claimed revenue uplifts, but provide no controlled experiment, peer review, or quantified performance comparisons against baselines.
Preprint. Allegro.com e-commerce users and product catalog.. Intervention: AlleCompanion retrieval framework with Category Adapter and ComCat multi-source complementary categories mapping.. Allegro.com (Poland-based platform); specific geographic scope not detailed..
System serves over 20 million active users monthly Claims significant uplifts in attributed GMV for organic discovery and sponsored placements, but no specific figures reported Combines Two Tower neural architecture with Category Adapter and multi-source ComCat mapping to filter co-purchase noise
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
A production deployment report of a recommendation system with claimed business metrics but no controlled experimental validation, peer review, or comparison against established baselines.
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When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic tripod, or another camera body? Complementary Product Recommendation is vital for comprehensive basket building, yet standard models often fail to distinguish between items that are merely bought together and those that truly work together. In this paper, we present AlleCompanion: a production-scale retrieval framework deployed at Allegro.com that transforms noisy behavioural signals into precise semantic compatibility. We mitigate the intrinsic noise in large-scale co-purchase traffic by combining data-level filtering heuristics with a category-constrained Two Tower architecture. Within this framework, the Category Adapter guides the model in the embedding space, constraining candidates within logically complementary boundaries. Since modelling authentic user behaviour at scale is inherently difficult, we introduce ComCat, a multi-source Complementary Categories Mapping. ComCat acts as a translational layer that distils meaningful patterns from noisy traffic into a maintainable and controllable solution, integrating expert rules, human-in-the-loop feedback, LLM-based reasoning, and statistical mining. Our experimental results demonstrate that combining explicit category-level constraints with neural architectures effectively filters out co-purchase noise to surface recommendations that satisfy real-world user needs. Serving over 20 million active users monthly, the framework delivers significant uplifts in attributed GMV for organic discovery and drives substantial revenue growth in sponsored placements.
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