arXiv AI
Jun 10
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
arXiv:2606. 10357v1 Announce Type: cross Abstract: Cross-domain recommendation is a core problem in content-to-e-commerce platforms.
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
arXiv:2606. 00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback.
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arXiv Machine Learning
Jun 10
Generative Archetype-Grounded Item Representations for Sequential Recommendation
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
Hugging Face Trending Papers
Jun 9
Generative Archetype-Grounded Item Representations for Sequential Recommendation
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 Computation and Language
Sep 11
LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains
LLMAR is a tuning‑free recommendation framework designed for sparse, text‑rich industrial B2B domains. It transforms user behavioral history into structured semantic motives using LLM inference, employs a reflection loop to self‑correct hallucinations, and operates cost‑effectively with asynchronous batch processing. Experiments on MovieLens‑1M, Amazon Prime Pantry, and a construction risk dataset show LLMAR surpasses state‑of‑the‑art learning models, achieving up to a 54.6% nDCG@10 improvement while keeping inference costs around $1 per 1,000 users.
arXiv AI
Jun 2
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
arXiv:2602. 07298v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are crucial for guiding research and optimizing resource allocation.
arXiv Machine Learning
Jun 17
On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.
arXiv AI
Sep 25
Cross-Country Code-Mixing for Generative Recommendation
Cross-Country Code-Mixing for Generative Recommendation (CMRec) is a framework that enhances generative recommendation across different countries by injecting cross-country supervision at the data level. It learns a shared semantic codebook from multi-modal content and behavioral co-occurrence, then synthesizes mixed-country sequences through token-level substitutions that respect both static and dynamic constraints. A context-aware loss reweights these mixed samples based on their plausibility, leading to improved recommendation quality in data-sparse countries while maintaining performance in data-rich markets, as demonstrated by significant gains in advertising revenue and orders in real-world e-commerce experiments.
arXiv AI
Aug 14
Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
arXiv:2608. 12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions.
arXiv Machine Learning
Aug 17
Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
arXiv:2604. 05379v2 Announce Type: replace-cross Abstract: The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences.
arXiv AI
Jun 3
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
arXiv AI
Aug 3
Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding
arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.
arXiv Computation and Language
Aug 25
A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework
The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.