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: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: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.
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
arXiv:2608.30399v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequ...
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.
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. 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:2607. 22518v1 Announce Type: cross Abstract: In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems.
The paper studies candidate generation for alternative vacation rental recommendations, comparing collaborative filtering, shallow embeddings, and graph neural network (GNN) methods on a platform with over 2 million active properties. A hybrid model that combines item-based collaborative filtering with GNN-based retrieval achieves a 14.8% higher Recall@300 than the best baseline, leveraging each method’s strengths: collaborative filtering for well-interacted properties and GNNs for diverse, cold-start alternatives. The authors also show that stronger candidate pools improve downstream ranking quality, though the exact impact is intertwined with ranker training.
arXiv:2606. 25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors.