The paper introduces DUMoE, a drift‑aware multimodal user representation framework that models user preferences over time by integrating static profiles, short‑term signals, and long‑term dependencies. It employs a sparse mixture‑of‑experts interest adapter, where each expert captures a distinct latent interest and a gating network selects relevant experts for each user. A three‑stage training strategy decouples backbone learning, expert specialization, and gating optimization, and experiments on real social media data demonstrate that DUMoE outperforms existing methods in user interest and interaction prediction.
By Ziqing Qian, Haohang Chen, Shengqi Dang, Yuhan Xiong, Canyu Shen, Jiaying Lei, Nan Cao
arXiv:2606. 01352v1 Announce Type: new Abstract: Watch time has emerged as a pivotal metric for optimizing deep user engagement in short-video recommender systems.
By Hongxu Ma, Han Zhou, Chenghou Jin, Jie Zhang, Xiaoyu Yang, Chunjie Chen, Jihong Guan, Shuigeng Zhou
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
M3TR is a temporal retrieval‑enhanced multi‑modal framework for predicting micro‑video popularity. It introduces a Mamba‑Hawkes Process module to model user feedback as self‑exciting events, capturing long‑range temporal dependencies. A temporal‑aware retrieval engine then identifies historically relevant videos by combining multi‑modal content similarity with popularity trajectory similarity, augmenting the target video’s features for improved prediction accuracy.
By Jiacheng Lu, Weijian Wang, Mingyuan Xiao, Yang Hua, Tao Song, Bo Peng, Cheng Hua, Haibing Guan
arXiv:2607. 29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of multimodal signals and adaptation to evolving user intent.
By Jiping Liu, Zhongmin Zhang, Zisen Sang, Zhijia Fang, Tao Ouyang, Ma Jiang, Shaopeng Liang, Zeyang Hou, Guodong Cao, Jia Jia
arXiv:2609.37533v1 Announce Type: new
Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically...
By Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive personalization demands a harder capability -- inferring what users care about from the multimodal traces they naturally leave behind.
arXiv:2608. 04695v1 Announce Type: cross Abstract: Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distributed, and highly non-IID.
By Priyanka Nihalchandani, Naman Srivastava, Varun Ojha, Pandarasamy Arjunan
arXiv:2607. 22603v1 Announce Type: new Abstract: Personalized multimodal large language models (MLLMs) aim to generate user-specific responses, but existing methods mainly rely on profile-level information and overlook diverse user preferences.
By Fan Lyu, Wenqi Zhang, Joost van de Weijer
arXiv:2606. 07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge.
By Fatema Siddika, Md Anwar Hossen, Tanwi Mallick, Ali Jannesari
MURAL is a multimodal recommendation framework that replaces static similarity graphs with a dynamic topology discovery process. It uses an Adaptive Edge Learner to find latent item-item correlations efficiently and an Uncertainty-Aware Fusion module to down‑weight noisy modality signals based on aleatoric uncertainty. The model also incorporates a contrastive teacher‑student alignment to stabilize training and has been shown to outperform state‑of‑the‑art baselines on large‑scale TikTok and Amazon datasets, providing both higher accuracy and interpretability.
By Ahmad Mousavi (Department of Mathematics,Statistics American University), Majid Alikhani (Independent Researcher), Yeon-Chang Lee (Department of Computer Science,Engineering Ulsan National Institute of Science,Technology), Roberto Corizzo (Department of Computer Science American University), Yeganeh Abdollahinejad (Department of Biosystems,Agricultural Engineering Michigan State University)
arXiv:2609.38140v1 Announce Type: cross
Abstract: Mixture-of-Experts (MoE), popularized by large language models, is a promising paradigm for scaling visual generative models. However, conventional t...
By Yu Xu, Yuxin Zhang, Xiao Yang, Haotian Yang, Yizhi Wang, Xinwei Huang, Minxuan Lin, Angtian Wang, Chongyang Ma, Fan Tang