19 citations · 36 across the 15 of their papers we have counts for
16 papers
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…
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…
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,…
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…
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…
Brain-Language Model Alignment: Insights into the Platonic Hypothesis and Intermediate-Layer Advantage
Ángela López-Cardona, Sebastián Idesis, Mireia Masias-Bruns +2
Do brains and language models converge toward the same internal representations of the world? Recent years have seen a rise in studies of neural activations and model alignment. In…
Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns +4
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions
Angela Lopez-Cardona, Sebastian Idesis, Ioannis Arapakis
Recently, the integration of cognitive neuroscience in Natural Language Processing (NLP) has gained significant attention. This article provides a critical and timely overview of r…
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Junchen Fu, Yongxin Ni, Joemon M. Jose +4
In this paper, we explore a less-studied yet practically important problem: how to efficiently and effectively adapt multiple (2) multimodal foundation models (MFMs) for the seq…
OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses
Angela Lopez-Cardona, Sebastian Idesis, Miguel Barreda-Ángeles +2
While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignme…
LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation
Junchen Fu, Xuri Ge, Kaiwen Zheng +5
In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier is using large language models (LLMs)…
Large Language Model driven Policy Exploration for Recommender Systems
Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis +1
Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL po…
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is com…
Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models
Angela Lopez-Cardona, Carlos Segura, Alexandros Karatzoglou +2
Advancements in Natural Language Processing (NLP), have led to the emergence of Large Language Models (LLMs) such as GPT, Llama, Claude, and Gemini, which excel across a range of t…
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
Mario Villaizán-Vallelado, Matteo Salvatori, Carlos Segura +1
Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and dec…