Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions. Despite its theoretical simplicity, the practical deployment of a sequence model in production is non-trivial due to complexity of the sequence and sparse labels.
arXiv:2606. 19108v1 Announce Type: new Abstract: Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions.
arXiv:2609.16407v1 Announce Type: cross Abstract: On a delivery platform, personalized store ranking greatly influences what users find and order. Unlike digital-only domains, candidate stores are lo...
arXiv:2607. 09877v1 Announce Type: new Abstract: Vacation rental marketplaces face a structural imbalance on the supply side: a small fraction of properties receive most user interactions, while the long tail of new, niche, and seasonal listings generates too little behavioral signal for collaborative filtering to serve effectively.
arXiv:2607. 12246v1 Announce Type: new Abstract: Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a large fraction of users lack sufficient history for effective recommendation, spanning both paid and organic channels.
arXiv:2608. 10562v1 Announce Type: new Abstract: Not all clicks are equal.
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.
arXiv:2608. 15877v1 Announce Type: new Abstract: Search and recommendation serve a shared discovery objective but encode intent differently.
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.
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. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.
arXiv:2606. 31693v1 Announce Type: cross Abstract: The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents.