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20232026
most citedExploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

41 citations · 108 across the 26 of their papers we have counts for

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Showing 2026Show all

17 papers · 1 filter

cond-mat.quant-gas2026

Analog Cosmology in a Quenched Bose-Einstein Condensate Bubble

Ruotong Zhai, Brendan Rhyno, Zain Mehdi +3

Recent interest and realization of ultracold bosons confined in shell-shaped, or bubble, traps have opened up several directions in the study of Bose-Einstein condensates (BECs) in…

cs.IR2026

Optimizing VLP-aligned Multimodal Intent Representation with Correct Visual Instantiation for Zero-Shot Composed Image Retrieval

Xuri Ge, Chunhao Wang, Junchen Fu +7

ZS-CIR aims to retrieve a target image from a reference image and a modification text without paired supervision, typically by encoding composed queries as text-dominant representa…

cs.IR2026

What Makes a Good Semantic ID for Generative Recommendation? A Reproducibility Study

Yufei Chen, Junchen Fu, Jujia Zhao +2

Generative recommendation has emerged as an active research direction, where items are commonly represented by semantic IDs (SIDs): discrete codes generated token by token. Despite…

cs.CV2026

A Comprehensive Review of Multimodal Facial State Analysis: Tasks, Methods, and Resources

Xuri Ge, Tianshuo Zhang, Ruihan Li +6

Facial state analysis plays a crucial role in understanding human expressions, psychological modeling, and human computer interaction. Traditional unimodal vision-based methods are…

cs.IR2026

Recommender System as Slow and Fast Thinkers

Zichen Yuan, Xiaoxuan Dong, Linkun Dai +8

Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments. In particula…

cs.IR2026

Risk-Aware Reranking for Agentic Tool Retrieval

Qinfei Li, Xiaoxuan Dong, Jin Zhang +6

Tool retrieval determines which external tools are exposed to an LLM agent for a user query or task, making retrieval a critical pre-execution safety boundary. Unlike document retr…

cs.CV2026

Aligning Human Sense: Calibrated Distributional Reward Learning for Video Generation

Nai-Xin Zhai, Weihua Cheng, Dexu Yu +9

Video generation is central to AI-powered content creation. Aligning generated videos with human preferences is a key criterion for evaluating generation quality. Despite significa…

cs.IR2026

Role of Personality in Conversational Information Seeking

Abdisalam Abukar, Junchen Fu, Chengli Zhai +1

Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does m…

cs.AI2026

Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?

Kaiwen Zheng, Junchen Fu, Wenhao Deng +3

Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable descriptio…

cs.CL2026

Activation Steering with Jungian Cognitive Functions

Liu Zai, Yumeng Wang, Junchen Fu +2

Controlling combinations of personality-related tendencies is important for adapting large language models to diverse interaction needs. But successful single-trait steering does n…

cs.IR2026

RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation

Wenhao Deng, Junchen Fu, Hanwen Du +6

Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent re…

cs.IR2026

Stream-aware Side Adaptation for Large Pre-trained Multimodal Embedding Models in Sequential Recommendation

Junchen Fu, Kaiwen Zheng, Ioannis Arapakis +4

Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item re…

cs.AI2026

SkillAudit: From Fixed-Suite Benchmarking to Skill-Centered Assessment

Dexu Yu, Youhua Li, Zhaoyang Guan +12

Agent skills have become a practical way to extend large language model agents, but the growing skill ecosystem still lacks a reliable way to judge whether a skill is worth deployi…

cs.IR2026

The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval

Junchen Fu, Xuri Ge, Xin Xin +6

Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA,…

cs.MM2026

Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues

Junchen Fu, Wenhao Deng, Kaiwen Zheng +5

Missing-modality information on e-commerce platforms, such as absent product images or textual descriptions, often arises from annotation errors or incomplete metadata, impairing b…

cs.IR2026

Differentiable Semantic ID for Generative Recommendation

Junchen Fu, Xuri Ge, Alexandros Karatzoglou +4

Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as…

cs.CV2026

Focal-RegionFace: Generating Fine-Grained Multi-attribute Descriptions for Arbitrarily Selected Face Focal Regions

Kaiwen Zheng, Junchen Fu, Songpei Xu +4

In this paper, we introduce an underexplored problem in facial analysis: generating and recognizing multi-attribute natural language descriptions, containing facial action units (A…