AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search

Modern Search Engine Results Pages (SERPs) present complex layouts where multiple elements compete for visibility. Attention modelling is crucial for optimising web design and computational advertising, whereas attention metrics can inform ad placement and revenue strategies. We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. AdSight uses a novel Transformer-based sequence-to-sequence architecture where the encoder processes cursor trajectory embeddings, and the decoder incorporates slot-specific features, enabling robust attention prediction across various SERP layouts. We evaluate our approach on two Machine Learning tasks: (1)~\emph{regression}, to predict fixation times and counts; and (2)~\emph{classification}, to determine some slot types were noticed. Our findings demonstrate the model's ability to predict attention with unprecedented precision, offering actionable insights for researchers and practitioners.

The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval

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, GPT, Mistral, and CLIP. These models have achieved remarkable performance across various tasks of multimodal information retrieval (MIR), including web search, cross-modal retrieval, and recommender systems, etc. However, due to their enormous parameter sizes, significant efficiency challenges emerge across training, deployment, and inference stages when adapting these models' representation for IR tasks. These challenges present substantial obstacles to the practical adaptation of foundation models for representation learning in information retrieval tasks. To address these pressing issues, we propose organizing the first EReL@MIR workshop at the Web Conference 2025, inviting participants to explore novel solutions, emerging problems, challenges, efficiency evaluation metrics and benchmarks. This workshop aims to provide a platform for both academic and industry researchers to engage in discussions, share insights, and foster collaboration toward achieving efficient and effective representation learning for multimodal information retrieval in the era of large foundation models.

CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation

Multimodal Foundation Models (MFMs) excel at representing diverse raw modalities (e.g., text, images, audio, videos, etc.). As recommender systems increasingly incorporate these modalities, leveraging MFMs to generate better representations has great potential. However, their application in sequential recommendation remains largely unexplored. This is primarily because mainstream adaptation methods, such as Fine-Tuning and even Parameter-Efficient Fine-Tuning (PEFT) techniques (e.g., Adapter and LoRA), incur high computational costs, especially when integrating multiple modality encoders, thus hindering research progress. As a result, it remains unclear whether we can efficiently and effectively adapt multiple (>2) MFMs for the sequential recommendation task. To address this, we propose a plug-and-play Cross-modal Side Adapter Network (CROSSAN). Leveraging the fully decoupled side adapter-based paradigm, CROSSAN achieves high efficiency while enabling cross-modal learning across diverse modalities. To optimize the final stage of multimodal fusion across diverse modalities, we adopt the Mixture of Modality Expert Fusion (MOMEF) mechanism. CROSSAN achieves superior performance on the public datasets for adapting four foundation models with raw modalities. Performance consistently improves as more MFMs are adapted. We will release our code and datasets to facilitate future research.

Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions

Recently, the integration of cognitive neuroscience in Natural Language Processing (NLP) has gained significant attention. This article provides a critical and timely overview of recent advancements in leveraging cognitive signals, particularly Eye-tracking (ET) signals, to enhance Language Models (LMs) and Multimodal Large Language Models (MLLMs). By incorporating user-centric cognitive signals, these approaches address key challenges, including data scarcity and the environmental costs of training large-scale models. Cognitive signals enable efficient data augmentation, faster convergence, and improved human alignment. The review emphasises the potential of ET data in tasks like Visual Question Answering (VQA) and mitigating hallucinations in MLLMs, and concludes by discussing emerging challenges and research trends.

A Comparative Study of Scanpath Models in Graph-Based Visualization

Information Visualization (InfoVis) systems utilize visual representations to enhance data interpretation. Understanding how visual attention is allocated is essential for optimizing interface design. However, collecting Eye-tracking (ET) data presents challenges related to cost, privacy, and scalability. Computational models provide alternatives for predicting gaze patterns, thereby advancing InfoVis research. In our study, we conducted an ET experiment with 40 participants who analyzed graphs while responding to questions of varying complexity within the context of digital forensics. We compared human scanpaths with synthetic ones generated by models such as DeepGaze, UMSS, and Gazeformer. Our research evaluates the accuracy of these models and examines how question complexity and number of nodes influence performance. This work contributes to the development of predictive modeling in visual analytics, offering insights that can enhance the design and effectiveness of InfoVis systems.

OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses

While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignment methods rely primarily on explicit feedback, eye-tracking (ET) data offers insights into real-time cognitive processing during reading. In this paper, we present OASST-ETC, a novel eye-tracking corpus capturing reading patterns from 24 participants, while evaluating LLM-generated responses from the OASST1 dataset. Our analysis reveals distinct reading patterns between preferred and non-preferred responses, which we compare with synthetic eye-tracking data. Furthermore, we examine the correlation between human reading measures and attention patterns from various transformer-based models, discovering stronger correlations in preferred responses. This work introduces a unique resource for studying human cognitive processing in LLM evaluation and suggests promising directions for incorporating eye-tracking data into alignment methods. The dataset and analysis code are publicly available.

LLMPopcorn: An Empirical Study of LLMs as Assistants for Popular Micro-video Generation

Popular Micro-videos, dominant on platforms like TikTok and YouTube, hold significant commercial value. The rise of high-quality AI-generated content has spurred interest in AI-driven micro-video creation. However, despite the advanced capabilities of large language models (LLMs) like ChatGPT and DeepSeek in text generation and reasoning, their potential to assist the creation of popular micro-videos remains largely unexplored. In this paper, we conduct an empirical study on LLM-assisted popular micro-video generation (LLMPopcorn). Specifically, we investigate the following research questions: (i) How can LLMs be effectively utilized to assist popular micro-video generation? (ii) To what extent can prompt-based enhancements optimize the LLM-generated content for higher popularity? (iii) How well do various LLMs and video generators perform in the popular micro-video generation task? By exploring these questions, we show that advanced LLMs like DeepSeek-V3 enable micro-video generation to achieve popularity comparable to human-created content. Prompt enhancements further boost popularity, and benchmarking highlights DeepSeek-V3 and DeepSeek-R1 among LLMs, while LTX-Video and HunyuanVideo lead in video generation. This pioneering work advances AI-assisted micro-video creation, uncovering new research opportunities. We will release the code and datasets to support future studies.

Large Language Model driven Policy Exploration for Recommender Systems

Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Additionally, excessive focus on exploiting short-term relevant items can hinder exploration, leading to suboptimal recommendations and negatively impacting long-term user gains. Online RL-based RS also face challenges in production deployment, due to the risks of exposing users to untrained or unstable policies. Large Language Models (LLMs) offer a promising solution to mimic user objectives and preferences for pre-training policies offline to enhance the initial recommendations in online settings. Effectively managing distribution shift and balancing exploration are crucial for improving RL-based RS, especially when leveraging LLM-based pre-training. To address these challenges, we propose an Interaction-Augmented Learned Policy (iALP) that utilizes user preferences distilled from an LLM. Our approach involves prompting the LLM with user states to extract item preferences, learning rewards based on feedback, and updating the RL policy using an actor-critic framework. Furthermore, to deploy iALP in an online scenario, we introduce an adaptive variant, A-iALP, that implements a simple fine-tuning strategy (A-iALP$_{ft}$), and an adaptive approach (A-iALP$_{ap}$) designed to mitigate issues with compromised policies and limited exploration. Experiments across three simulated environments demonstrate that A-iALP introduces substantial performance improvements

Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation

Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt these models, studies often prioritize parameter efficiency, neglecting GPU memory and training speed. To address this, we introduced the IISAN framework, significantly enhancing efficiency. However, IISAN was limited to symmetrical MFMs and identical text and image encoders, preventing the use of state-of-the-art Large Language Models. To overcome this, we developed IISAN-Versa, a versatile plug-and-play architecture compatible with both symmetrical and asymmetrical MFMs. IISAN-Versa employs a Decoupled PEFT structure and utilizes both intra- and inter-modal adaptation. It effectively handles asymmetry through a simple yet effective combination of group layer-dropping and dimension transformation alignment. Our research demonstrates that IISAN-Versa effectively adapts large text encoders, and we further identify a scaling effect where larger encoders generally perform better. IISAN-Versa also demonstrates strong versatility in our defined multimodal scenarios, which include raw titles and captions generated from images and videos. Additionally, IISAN-Versa achieved state-of-the-art performance on the Microlens public benchmark. We will release our code and datasets to support future research.

Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models

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 tasks but require extensive fine-tuning to align their outputs with human expectations. A widely used method for achieving this alignment is Reinforcement Learning from Human Feedback (RLHF), which, despite its success, faces challenges in accurately modelling human preferences. In this paper, we introduce GazeReward, a novel framework that integrates implicit feedback -- and specifically eye-tracking (ET) data -- into the Reward Model (RM). In addition, we explore how ET-based features can provide insights into user preferences. Through ablation studies we test our framework with different integration methods, LLMs, and ET generator models, demonstrating that our approach significantly improves the accuracy of the RM on established human preference datasets. This work advances the ongoing discussion on optimizing AI alignment with human values, exploring the potential of cognitive data for shaping future NLP research.