arXiv:2607. 24764v1 Announce Type: new Abstract: The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents.
arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.
DeepAffinity is a model designed to predict eCommerce users’ future preferences for product aspects such as brand, size, and color, treating this as a temporal prediction problem. It uses small language models with structured prompts and specialized prediction heads, outperforming standard generative fine‑tuning and general‑purpose open‑source LLMs that lack task‑specific tuning. The approach improves recommendation quality on a large multinational eCommerce platform.
The paper introduces AlleCompanion, a large‑scale retrieval framework for complementary product recommendations at Allegro.com. It addresses the challenge of noisy co‑purchase data by combining data‑level filtering, a category‑constrained Two Tower architecture, and a multi‑source Complementary Categories Mapping (ComCat) that incorporates expert rules, human feedback, LLM reasoning, and statistical mining. Experiments show that these explicit category constraints and neural models effectively reduce noise, improving recommendation relevance and driving significant GMV growth for both organic discovery and sponsored placements.
The paper introduces AlleCompanion, a large‑scale retrieval framework used by Allegro.com to improve complementary product recommendations. It tackles the problem of noisy co‑purchase data by applying data‑level filtering, a category‑constrained Two Tower architecture, and a Category Adapter that limits candidates to logically complementary categories. The system also incorporates a multi‑source Complementary Categories Mapping (ComCat) that blends expert rules, human feedback, LLM reasoning, and statistical mining to refine recommendations, resulting in higher GMV for organic discovery and increased revenue from sponsored placements.
arXiv:2607. 25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline.
arXiv:2606. 10357v1 Announce Type: cross Abstract: Cross-domain recommendation is a core problem in content-to-e-commerce platforms.
arXiv:2608. 11604v1 Announce Type: new Abstract: Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable supervision for improving these agents.
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items.
arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.
LIGE‑GR is a framework that transitions traditional ranking‑based recommender systems to a generative, listwise approach inspired by large language models. It extends existing pointwise recommendation models into a listwise generation system, enabling sequence‑level optimization without overhauling the entire infrastructure. Experiments on Instagram Reels and Facebook Video show modest gains in user time spent—1.14 % and 0.72 % respectively—while adding only slight inference overhead.
arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...
arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.