arXiv AI

MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.

arXiv AI

Jul 24

Probabilistic Residual Learning for Online Recommendations

arXiv:2607. 20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items.

By Wenyuan Wang, Yusong Zhao, Zihao Xu, Hengyi Wang, Qi Xu, Zhigang Hua, Yan Xie, Yi Wang, Zihao Zhao, Bo Long, Chengzhi Mao, Shuang Yang, Hengguan Huang, Hao Wang

arXiv AI

Jun 4

Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling

arXiv:2606. 04284v1 Announce Type: cross Abstract: Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values.

By Yifan Wang, Jinyi Mu, Mayank Jobanputra, Yu Wang, Ji-Ung Lee, Soyoung Oh, Isabel Valera, Vera Demberg

llms reinforcement-learning safety

arXiv AI

Jun 2

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.

By Xiangyu Wang, Yawen He, Shivendra Pratap Singh, Han Huang, Mengtong Hu, Sharath Ciddu, Yi-Hsuan Hsieh, Erik Groving, Yi Ding, Jieming Di, Tony Wang, Min Yun, Xiaoyu Chen, Ling Leng, Rob Malkin

llms efficiency

Hugging Face Trending Papers

Jul 23

Probabilistic Residual Learning for Online Recommendations

Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities.

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.

By Zhuohang Jiang, Yuxin Chen, Shijie Wang, Haohao Qu, Zhou Jindong, Wenqi Fan, Li Qing, Dongxu Liang, Jun Wang

llms efficiency benchmarks

arXiv AI

Jun 2

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks

arXiv:2506. 16114v3 Announce Type: replace-cross Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios.

By Yejing Wang, Shengyu Zhou, Jinyu Lu, Qidong Liu, Xinhang Li, Wenlin Zhang, Feng Li, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng, Xiangyu Zhao

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 7

Personalized Causal Recourse: A Human-In-The-Loop Approach

arXiv:2607. 03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios.

By Denise Tampieri, Giovanni De Toni, Paolo Giudici

arXiv AI

1d ago

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.

By Vahid Azizi, Fatemeh Koochaki

llms rag fine-tuning

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv Machine Learning

Jul 17

Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest

arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.

By Junpeng Hou, XianXing Zhang, Sai Xiao, Derek Cheng, Darren Reger, Olafur Gudmundsson, Mehdi Ben Ayed, Zhiqing Rao, Huizhong Duan

arXiv AI

Jul 28

CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation

arXiv:2607. 23647v1 Announce Type: cross Abstract: Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring preferences, transient intent, and exposure-induced behavior into one profile.

By Gengyu Zhan

llms multimodal benchmarks safety

arXiv AI

Aug 3

MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation

arXiv:2607. 29177v1 Announce Type: cross Abstract: Utility data (e.

By Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang

diffusion benchmarks

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