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:2610.07810v1 Announce Type: new Abstract: Search filters help guests navigate vast catalogs in two-sided marketplaces like Airbnb, and recommending the right filters can meaningfully lift booki...
arXiv:2607. 13328v1 Announce Type: cross Abstract: Personalized recommendation systems are central to modern e-commerce and retail platforms, but they typically rely on centralized storage of detailed user interaction data, creating significant privacy and regulatory challenges.
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:2608. 02052v1 Announce Type: new Abstract: Human mobility prediction models, which forecast the next location in a user's trajectory, are increasingly deployed in urban analytics, navigation, and personalized services.
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:2609.36153v1 Announce Type: cross Abstract: B2B advertising targets a viewer's professional attributes (employer size and industry, function, seniority) and has obtained them by matching identi...
arXiv:2607. 28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent.
The paper introduces PMFRec, a federated cold-start recommendation framework that addresses personalization, compositionality, and communication inefficiencies. PMFRec generates user-specific item representations from attribute features, employs a global multi-view encoder with adaptive gating and orthogonality to capture complementary semantics, and fuses collaborative and attribute knowledge into a single exchanged representation. Experiments on real-world datasets demonstrate that PMFRec outperforms strong baselines in cold-item recommendation while improving user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy.
arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.
arXiv:2607. 01530v1 Announce Type: cross Abstract: Understanding user intent is fundamental to delivering relevant search results in e-commerce.
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.