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Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey
Iratxe Pinedo, Mikel Larra\~naga, Ana Arruarte
https://arxiv.org/abs/2508.08828 https://…

Recent Advances and Trends in Research Paper Recommender Systems: A Comprehensive Survey
As the volume of scientific publications grows exponentially, researchers increasingly face difficulties in locating relevant literature. Research Paper Recommender Systems have become vital tools to mitigate this information overload by delivering personalized suggestions. This survey provides a comprehensive analysis of Research Paper Recommender Systems developed between November 2021 and December 2024, building upon prior reviews in the field. It presents an extensive overview of the techni…

EchoAid: Enhancing Livestream Shopping Accessibility for the DHH Community
Zeyu Yang, Zheng Wei, Yang Zhang, Xian Xu, Changyang He, Muzhi Zhou, Pan Hui
https://arxiv.org/abs/2508.08020

EchoAid: Enhancing Livestream Shopping Accessibility for the DHH Community
Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and overload. To tackle these challenges, we developed \textit{EchoAid}, a mobile app designed to improve the livestream shopping experience for DHH users. \textit{EchoAid} utilizes advanced speech-to-text conversion, Rapid Serial Visual Presentation (RSVP) technology, and Large Language Models (LLMs) to simplify the comple…

PolicyStory: Leveraging Large Language Models to Generate Comprehensible Summaries of Policy-News in India
Aatif Nisar Dar, Aditya Raj Singh, Anirban Sen
https://arxiv.org/abs/2509.08218

PolicyStory: Leveraging Large Language Models to Generate Comprehensible Summaries of Policy-News in India
In the era of information overload, traditional news consumption through both online and print media often fails to provide a structured and longitudinal understanding of complex sociopolitical issues. To address this gap, we present PolicyStory, an information tool designed to offer lucid, chronological, and summarized insights into Indian policy issues. PolicyStory collects news articles from diverse sources, clusters them by topic, and generates three levels of summaries from longitudinal me…

Whom We Trust, What We Fear: COVID-19 Fear and the Politics of Information
Daniele Baccega, Paolo Castagno, Antonio Fern\'andez Anta, Juan Marcos Ramirez, Matteo Sereno
https://arxiv.org/abs/2508.20146

Whom We Trust, What We Fear: COVID-19 Fear and the Politics of Information
The COVID-19 pandemic triggered not only a global health crisis but also an infodemic, an overload of information from diverse sources influencing public perception and emotional responses. In this context, fear emerged as a central emotional reaction, shaped by both media exposure and demographic factors. In this study, we analyzed the relationship between individuals' self-reported levels of fear about COVID-19 and the information sources they rely on, across nine source categories, including…

BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications
Andr\'es Fern\'andez Garc\'ia, Javier de la Rosa, Julio Gonzalo, Roser Morante, Enrique Amig\'o, Alejandro Benito-Santos, Jorge Carrillo-de-Albornoz, V\'ictor Fresno, Adrian Ghajari, Guillermo Marco, Laura Plaza, Eva S\'anchez Salido
https://

BOE-XSUM: Extreme Summarization in Clear Language of Spanish Legal Decrees and Notifications
The ability to summarize long documents succinctly is increasingly important in daily life due to information overload, yet there is a notable lack of such summaries for Spanish documents in general, and in the legal domain in particular. In this work, we present BOE-XSUM, a curated dataset comprising 3,648 concise, plain-language summaries of documents sourced from Spain's ``Bolet\'ın Oficial del Estado'' (BOE), the State Official Gazette. Each entry in the dataset includes a short summary, t…

AttenTrack: Mobile User Attention Awareness Based on Context and External Distractions
Yutong Lin, Suyuan Liu, Kaiwen Guo, Haohua Du, Chao Liu, Xiang-Yang Li
https://arxiv.org/abs/2509.01414

AttenTrack: Mobile User Attention Awareness Based on Context and External Distractions
In the mobile internet era, managing limited attention amid information overload is crucial for enhancing collaboration and information delivery. However, current attention-aware systems often depend on wearables or personalized data, limiting their scalability and cross-context adaptability. Inspired by psychological theories, we attempt to treat mobile notifications as naturally occurring external distractions and infer users' attention states based on their response behaviors and contextual …

Reduce-Rank Matrix Integer-Valued Autoregressive Model
Kaiyan Cui, Tianyun Guo, Suping Wang
https://arxiv.org/abs/2509.03338 https://arxiv.org/pdf/2509.033…

Reduce-Rank Matrix Integer-Valued Autoregressive Model
Integer-valued time series are widely present in many fields, such as finance, economics, disease transmission, and traffic flow. With data dimensions surging, the traditional multivariate generalized integer autoregressive (MGINAR) model faces parameter overload, poor interpretability, and structural information loss. Matrix integer-valued autoregression (MINAR) model captures row-column cross-correlations and reduces the number of parameters to be estimated. However, further growth in dimensi…

"Low Frequency Tweeters Have More to Say!" A New Approach to Identify Importance of Tweets
Gautam Khannaa, Yeliz Yesilada, Sukru Eraslan, Simon Harper
https://arxiv.org/abs/2509.03931

"Low Frequency Tweeters Have More to Say!" A New Approach to Identify Importance of Tweets
Twitter is one of the most popular social media platforms.With a large number of tweets, the activity feed of users becomes noisy, challenging to read, and most importantly tweets often get lost. We present a new approach to personalise the ranking of the tweets toward solving the problem of information overload which is achieved by analysing the relationship between the importance of tweets to the frequency at which the author tweets. The hypothesis tested is that "low-frequency tweeters have …

The Statistical Validation of Innovation Lens
Giacomo Radaelli, Jonah Lynch
https://arxiv.org/abs/2508.14139 https://arxiv.org/pdf/2508.14139

The Statistical Validation of Innovation Lens
Information overload and the rapid pace of scientific advancement make it increasingly difficult to evaluate and allocate resources to new research proposals. Is there a structure to scientific discovery that could inform such decisions? We present statistical evidence for such structure, by training a classifier that successfully predicts high-citation research papers between 2010-2024 in the Computer Science, Physics, and PubMed domains.

PARK: Personalized academic retrieval with knowledge-graphs
Pranav Kasela, Gabriella Pasi, Raffaele Perego
https://arxiv.org/abs/2507.13910 https://…

PARK: Personalized academic retrieval with knowledge-graphs
Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers' needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized acade…

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Zijian Zhang, Pan Chen, Fangshi Du, Runlong Ye, Oliver Huang, Michael Liut, Al\'an Aspuru-Guzik
https://arxiv.org/abs/2507.18945

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative st…

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva
https://arxiv.org/abs/2507.15460

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for n…

Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach
Sami Saeed Alghamdi (Open Lab, School of Computing, Newcastle University), Christopher Bull (Open Lab, School of Computing, Newcastle University), Ahmed Kharrufa (Open Lab, School of Computing, Newcastle University)
https://arxiv.o…

Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach
Many people learn programming independently from online resources and often report struggles in achieving their personal learning goals. Learners frequently describe their experiences as isolating and frustrating, challenged by abundant uncertainties, information overload, and distraction, compounded by limited guidance. At the same time, social media serves as a personal space where many engage in diverse self-regulation practices, including help-seeking, using external memory aids (e.g., self…

M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Lining Chen, Qingwen Zeng, Huaming Chen
https://arxiv.org/abs/2508.15262

M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant …

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, Haihua Chen
https://arxiv.org/abs/2507.11810

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and…

Adoption, usability and perceived clinical value of a UK AI clinical reference platform (iatroX): a mixed-methods formative evaluation of real-world usage and a 1,223-respondent user survey
Kolawole Tytler
https://arxiv.org/abs/2509.21188

Adoption, usability and perceived clinical value of a UK AI clinical reference platform (iatroX): a mixed-methods formative evaluation of real-world usage and a 1,223-respondent user survey
Clinicians face growing information overload from biomedical literature and guidelines, hindering evidence-based care. Retrieval-augmented generation (RAG) with large language models may provide fast, provenance-linked answers, but requires real-world evaluation. We describe iatroX, a UK-centred RAG-based clinical reference platform, and report early adoption, usability, and perceived clinical value from a formative implementation evaluation. Methods comprised a retrospective analysis of usage …

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
Ronald Carvalho Boadana, Ademir Guimar\~aes da Costa Junior, Ricardo Rios, F\'abio Santos da Silva
https://arxiv.org/abs/2508.11671

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
The growing availability of music on streaming platforms has led to information overload for users. To address this issue and enhance the user experience, increasingly sophisticated recommendation systems have been proposed. This work investigates the use of Large Language Models (LLMs) from the Gemini and LLaMA families, combined with intelligent agents, in a multi-agent personalized music recommendation system. The results are compared with a traditional content-based recommendation model, co…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
https://arxiv.org/abs/2508.13064 https:/…

Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and us…

Hypercomplex Prompt-aware Multimodal Recommendation
Zheyu Chen, Jinfeng Xu, Hewei Wang, Shuo Yang, Zitong Wan, Haibo Hu
https://arxiv.org/abs/2508.10753 https://

Hypercomplex Prompt-aware Multimodal Recommendation
Modern recommender systems face critical challenges in handling information overload while addressing the inherent limitations of multimodal representation learning. Existing methods suffer from three fundamental limitations: (1) restricted ability to represent rich multimodal features through a single representation, (2) existing linear modality fusion strategies ignore the deep nonlinear correlations between modalities, and (3) static optimization methods failing to dynamically mitigate the o…