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Unlike prompting and supervised fine-tuning (SFT), Rec-R1 directly optimizes LLM generation using feedback from a fixed black-box recommendation model, without relying on synthetic SFT data from proprietary models such as GPT-4o. This avoids the substantial cost and effort required for data distillation. To verify the effectiveness of Rec-R1, we evaluate it on two representative tasks: product search and sequential recommendation. Experimental results demonstrate that Rec-R1 not only consistently outperforms prompting- and SFT-based methods, but also achieves significant gains over strong discriminative baselines, even when used with simple retrievers such as BM25. Moreover, Rec-R1 preserves the general-purpose capabilities of the LLM, unlike SFT, which often impairs instruction-following and reasoning. 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href="/author/Jos%C3%A9%20Luis%20Redondo%20Garc%C3%ADa">Jos茅 Luis Redondo Garc铆a</a>, </span><span><a data-testid="paper-result-author" href="/author/Timothy%20Christopher%20Heath">Timothy Christopher Heath</a>, </span><span><a data-testid="paper-result-author" href="/author/Alice%20Wang">Alice Wang</a>, </span><span><a data-testid="paper-result-author" href="/author/Hugues%20Bouchard">Hugues Bouchard</a>, </span><span><a data-testid="paper-result-author" href="/author/Mounia%20Lalmas">Mounia Lalmas</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>In recent years, Large Language Models (LLMs) have enabled users to provide highly specific music recommendation requests using natural language prompts (e.g. "Can you recommend some old classics for slow dancing?"). In this setup, the recommended tracks are predicted by the LLM in an autoregressive way, i.e. the LLM generates the track titles one token at a time. While intuitive, this approach has several limitation. First, it is based on a general purpose tokenization that is optimized for words rather than for track titles. Second, it necessitates an additional entity resolution layer that matches the track title to the actual track identifier. Third, the number of decoding steps scales linearly with the length of the track title, slowing down inference. In this paper, we propose to address the task of prompt-based music recommendation as a generative retrieval task. Within this setting, we introduce novel, effective, and efficient representations of track identifiers that significantly outperform commonly used strategies. We introduce Text2Tracks, a generative retrieval model that learns a mapping from a user's music recommendation prompt to the relevant track IDs directly. Through an offline evaluation on a dataset of playlists with language inputs, we find that (1) the strategy to create IDs for music tracks is the most important factor for the effectiveness of Text2Tracks and semantic IDs significantly outperform commonly used strategies that rely on song titles as identifiers (2) provided with the right choice of track identifiers, Text2Tracks outperforms sparse and dense retrieval solutions trained to retrieve tracks from language prompts.<br/></p><div class="text-with-links"><span></span><span></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" 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data-testid="paper-result-author" href="/author/Arief%20Suriawinata">Arief Suriawinata</a>, </span><span><a data-testid="paper-result-author" href="/author/Laura%20Tafe">Laura Tafe</a>, </span><span><a data-testid="paper-result-author" href="/author/Saeed%20Hassanpour">Saeed Hassanpour</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) emerging as its most prevalent subtype. Among NSCLC patients, approximately 32.3% have mutations in the epidermal growth factor receptor (EGFR) gene. Osimertinib, a third-generation EGFR-tyrosine kinase inhibitor (TKI), has demonstrated remarkable efficacy in the treatment of NSCLC patients with activating and T790M resistance EGFR mutations. Despite its established efficacy, drug resistance poses a significant challenge for patients to fully benefit from osimertinib. The absence of a standard tool to accurately predict TKI resistance, including that of osimertinib, remains a critical obstacle. To bridge this gap, in this study, we developed an interpretable multimodal machine learning model designed to predict patient resistance to osimertinib among late-stage NSCLC patients with activating EGFR mutations, achieving a c-index of 0.82 on a multi-institutional dataset. This machine learning model harnesses readily available data routinely collected during patient visits and medical assessments to facilitate precision lung cancer management and informed treatment decisions. By integrating various data types such as histology images, next generation sequencing (NGS) data, demographics data, and clinical records, our multimodal model can generate well-informed recommendations. Our experiment results also demonstrated the superior performance of the multimodal model over single modality models (c-index 0.82 compared with 0.75 and 0.77), thus underscoring the benefit of combining multiple modalities in patient outcome prediction.<br/></p><div class="text-with-links"><span></span><span></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 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href="/author/Romain%20Rampa">Romain Rampa</a>, </span><span><a data-testid="paper-result-author" href="/author/Golnoosh%20Farnadi">Golnoosh Farnadi</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>Novelty modeling and detection is a core topic in Natural Language Processing (NLP), central to numerous tasks such as recommender systems and automatic summarization. It involves identifying pieces of text that deviate in some way from previously known information. However, novelty is also a crucial determinant of the unique perception of relevance and quality of an experience, as it rests upon each individual's understanding of the world. Social factors, particularly cultural background, profoundly influence perceptions of novelty and innovation. Cultural novelty arises from differences in salience and novelty as shaped by the distance between distinct communities. While cultural diversity has garnered increasing attention in artificial intelligence (AI), the lack of robust metrics for quantifying cultural novelty hinders a deeper understanding of these divergences. This gap limits quantifying and understanding cultural differences within computational frameworks. To address this, we propose an interdisciplinary framework that integrates knowledge from sociology and management. Central to our approach is GlobalFusion, a novel dataset comprising 500 dishes and approximately 100,000 cooking recipes capturing cultural adaptation from over 150 countries. By introducing a set of Jensen-Shannon Divergence metrics for novelty, we leverage this dataset to analyze textual divergences when recipes from one community are modified by another with a different cultural background. The results reveal significant correlations between our cultural novelty metrics and established cultural measures based on linguistic, religious, and geographical distances. 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style="display:none">Abstract:</span>Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.<br/></p><div class="text-with-links"><span></span><span></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 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</span><span><a data-testid="paper-result-author" href="/author/Tong%20Yu">Tong Yu</a>, </span><span><a data-testid="paper-result-author" href="/author/Xiwei%20Xu">Xiwei Xu</a>, </span><span><a data-testid="paper-result-author" href="/author/Liming%20Zhu">Liming Zhu</a>, </span><span><a data-testid="paper-result-author" href="/author/Lina%20Yao">Lina Yao</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>Large language model-based agents are increasingly used in recommender systems (Agent4RSs) to achieve personalized behavior modeling. Specifically, Agent4RSs introduces memory mechanisms that enable the agents to autonomously learn and self-evolve from real-world interactions. However, to the best of our knowledge, how robust Agent4RSs are remains unexplored. As such, in this paper, we propose the first work to attack Agent4RSs by perturbing agents' memories, not only to uncover their limitations but also to enhance their security and robustness, ensuring the development of safer and more reliable AI agents. Given the security and privacy concerns, it is more practical to launch attacks under a black-box setting, where the accurate knowledge of the victim models cannot be easily obtained. Moreover, the practical attacks are often stealthy to maximize the impact. To this end, we propose a novel practical attack framework named DrunkAgent. DrunkAgent consists of a generation module, a strategy module, and a surrogate module. The generation module aims to produce effective and coherent adversarial textual triggers, which can be used to achieve attack objectives such as promoting the target items. The strategy module is designed to `get the target agents drunk' so that their memories cannot be effectively updated during the interaction process. As such, the triggers can play the best role. Both of the modules are optimized on the surrogate module to improve the transferability and imperceptibility of the attacks. By identifying and analyzing the vulnerabilities, our work provides critical insights that pave the way for building safer and more resilient Agent4RSs. 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systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposing niche or novel content. Existing bias mitigation methods, such as Inverse Propensity Scoring (IPS) and Off- Policy Correction (OPC), primarily operate at the ranking stage or during training, lacking explicit real-time control over exposure dynamics. In this work, we introduce an exposure- aware retrieval scoring approach, which explicitly models item exposure probability and adjusts retrieval-stage ranking at inference time. Unlike prior work, this method decouples exposure effects from engagement likelihood, enabling controlled trade-offs between fairness and engagement in large-scale recommendation platforms. We validate our approach through online A/B experiments in a real-world video recommendation system, demonstrating a 25% increase in uniquely retrieved items and a 40% reduction in the dominance of over-popular content, all while maintaining overall user engagement levels. Our results establish a scalable, deployable solution for mitigating popularity bias at the retrieval stage, offering a new paradigm for bias-aware personalization.<br/></p><div class="text-with-links"><span></span><span><em>* <!-- -->2 pages<!-- -->聽</em><br/></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 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href="/author/Deshan%20Sumanathilaka">Deshan Sumanathilaka</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration, which must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems.<br/></p><div class="text-with-links"><span></span><span><em>* <!-- -->6 pages, 2 figures, 1 table<!-- -->聽</em><br/></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 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</span><span><a data-testid="paper-result-author" href="/author/Jiuyi%20Zhang">Jiuyi Zhang</a>, </span><span><a data-testid="paper-result-author" href="/author/Wanli%20Zhu">Wanli Zhu</a>, </span><span><a data-testid="paper-result-author" href="/author/Guanjin%20Li">Guanjin Li</a>, </span><span><a data-testid="paper-result-author" href="/author/Fangming%20Gu">Fangming Gu</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>Sequential Recommender Systems (SRS) aim to model sequential behaviors of users to capture their interests which usually evolve over time. Transformer-based SRS have achieved distinguished successes recently. However, studies reveal self-attention mechanism in Transformer-based models is essentially a low-pass filter and ignores high frequency information potentially including meaningful user interest patterns. This motivates us to seek better filtering technologies for SRS, and finally we find Discrete Wavelet Transform (DWT), a famous time-frequency analysis technique from digital signal processing field, can effectively process both low-frequency and high-frequency information. We design an adaptive time-frequency filter with DWT technique, which decomposes user interests into multiple signals with different frequency and time, and can automatically learn weights of these signals. Furthermore, we develop DWTRec, a model for sequential recommendation all based on the adaptive time-frequency filter. Thanks to fast DWT technique, DWTRec has a lower time complexity and space complexity theoretically, and is Proficient in modeling long sequences. Experiments show that our model outperforms state-of-the-art baseline models in datasets with different domains, sparsity levels and average sequence lengths. Especially, our model shows great performance increase in contrast with previous models when the sequence grows longer, which demonstrates another advantage of our model.<br/></p><div class="text-with-links"><span></span><span></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 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Al-Sinani</a>, </span><span><a data-testid="paper-result-author" href="/author/Chris%20J.%20Mitchell">Chris J. Mitchell</a></span></div><div class="Search_paper-detail-page-images-container__FPeuN"></div><p class="Search_paper-content__1CSu5 text-with-links"><span class="descriptor" style="display:none">Abstract:</span>This paper investigates the presence and impact of questionable, AI-generated academic papers on widely used preprint repositories, with a focus on their role in citation manipulation. Motivated by suspicious patterns observed in publications related to our ongoing research on GenAI-enhanced cybersecurity, we identify clusters of questionable papers and profiles. These papers frequently exhibit minimal technical content, repetitive structure, unverifiable authorship, and mutually reinforcing citation patterns among a recurring set of authors. To assess the feasibility and implications of such practices, we conduct a controlled experiment: generating a fake paper using GenAI, embedding citations to suspected questionable publications, and uploading it to one such repository (ResearchGate). Our findings demonstrate that such papers can bypass platform checks, remain publicly accessible, and contribute to inflating citation metrics like the H-index and i10-index. We present a detailed analysis of the mechanisms involved, highlight systemic weaknesses in content moderation, and offer recommendations for improving platform accountability and preserving academic integrity in the age of GenAI.<br/></p><div class="text-with-links"><span></span><span><em>* <!-- -->20 pages<!-- -->聽</em><br/></span></div><div class="Search_search-result-provider__uWcak">Via<img alt="arxiv icon" loading="lazy" width="56" height="25" decoding="async" data-nimg="1" class="Search_arxiv-icon__SXHe4" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=64&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Farxiv.41e50dc5.png&w=128&q=75"/></div><div class="Search_paper-link__nVhf_"><svg role="img" height="20" width="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg" style="margin-right:5px"><title>Github Icon</title><path d="M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12"></path></svg><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" aria-hidden="true" data-slot="icon" width="22" style="margin-right:10px;margin-top:2px"><path stroke-linecap="round" stroke-linejoin="round" d="M12 6.042A8.967 8.967 0 0 0 6 3.75c-1.052 0-2.062.18-3 .512v14.25A8.987 8.987 0 0 1 6 18c2.305 0 4.408.867 6 2.292m0-14.25a8.966 8.966 0 0 1 6-2.292c1.052 0 2.062.18 3 .512v14.25A8.987 8.987 0 0 0 18 18a8.967 8.967 0 0 0-6 2.292m0-14.25v14.25"></path></svg><a data-testid="paper-result-access-link" href="/paper/from-content-creation-to-citation-inflation-a">Access Paper or Ask Questions</a></div></section><div class="Search_seperator-line__4FidS"></div></div></section><section data-hydration-on-demand="true"></section></div><section data-hydration-on-demand="true"></section></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"results":[{"title":"Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning","content":"We propose Rec-R1, a general reinforcement learning framework that bridges large language models (LLMs) with recommendation systems through closed-loop optimization. Unlike prompting and supervised fine-tuning (SFT), Rec-R1 directly optimizes LLM generation using feedback from a fixed black-box recommendation model, without relying on synthetic SFT data from proprietary models such as GPT-4o. This avoids the substantial cost and effort required for data distillation. To verify the effectiveness of Rec-R1, we evaluate it on two representative tasks: product search and sequential recommendation. Experimental results demonstrate that Rec-R1 not only consistently outperforms prompting- and SFT-based methods, but also achieves significant gains over strong discriminative baselines, even when used with simple retrievers such as BM25. Moreover, Rec-R1 preserves the general-purpose capabilities of the LLM, unlike SFT, which often impairs instruction-following and reasoning. These findings suggest Rec-R1 as a promising foundation for continual task-specific adaptation without catastrophic forgetting.","authors":["Jiacheng Lin","Tian Wang","Kun Qian"],"pdf_url":"http://arxiv.org/abs/2503.24289","paper_id":"2503.24289","link":"/paper/rec-r1-bridging-generative-large-language","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"rec-r1-bridging-generative-large-language"},{"title":"Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval","content":"In recent years, Large Language Models (LLMs) have enabled users to provide highly specific music recommendation requests using natural language prompts (e.g. \"Can you recommend some old classics for slow dancing?\"). In this setup, the recommended tracks are predicted by the LLM in an autoregressive way, i.e. the LLM generates the track titles one token at a time. While intuitive, this approach has several limitation. First, it is based on a general purpose tokenization that is optimized for words rather than for track titles. Second, it necessitates an additional entity resolution layer that matches the track title to the actual track identifier. Third, the number of decoding steps scales linearly with the length of the track title, slowing down inference. In this paper, we propose to address the task of prompt-based music recommendation as a generative retrieval task. Within this setting, we introduce novel, effective, and efficient representations of track identifiers that significantly outperform commonly used strategies. We introduce Text2Tracks, a generative retrieval model that learns a mapping from a user's music recommendation prompt to the relevant track IDs directly. Through an offline evaluation on a dataset of playlists with language inputs, we find that (1) the strategy to create IDs for music tracks is the most important factor for the effectiveness of Text2Tracks and semantic IDs significantly outperform commonly used strategies that rely on song titles as identifiers (2) provided with the right choice of track identifiers, Text2Tracks outperforms sparse and dense retrieval solutions trained to retrieve tracks from language prompts.","authors":["Enrico Palumbo","Gustavo Penha","Andreas Damianou","Jos茅 Luis Redondo Garc铆a","Timothy Christopher Heath","Alice Wang","Hugues Bouchard","Mounia Lalmas"],"pdf_url":"http://arxiv.org/abs/2503.24193","paper_id":"2503.24193","link":"/paper/text2tracks-prompt-based-music-recommendation","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"text2tracks-prompt-based-music-recommendation"},{"title":"Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning","content":"Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) emerging as its most prevalent subtype. Among NSCLC patients, approximately 32.3% have mutations in the epidermal growth factor receptor (EGFR) gene. Osimertinib, a third-generation EGFR-tyrosine kinase inhibitor (TKI), has demonstrated remarkable efficacy in the treatment of NSCLC patients with activating and T790M resistance EGFR mutations. Despite its established efficacy, drug resistance poses a significant challenge for patients to fully benefit from osimertinib. The absence of a standard tool to accurately predict TKI resistance, including that of osimertinib, remains a critical obstacle. To bridge this gap, in this study, we developed an interpretable multimodal machine learning model designed to predict patient resistance to osimertinib among late-stage NSCLC patients with activating EGFR mutations, achieving a c-index of 0.82 on a multi-institutional dataset. This machine learning model harnesses readily available data routinely collected during patient visits and medical assessments to facilitate precision lung cancer management and informed treatment decisions. By integrating various data types such as histology images, next generation sequencing (NGS) data, demographics data, and clinical records, our multimodal model can generate well-informed recommendations. Our experiment results also demonstrated the superior performance of the multimodal model over single modality models (c-index 0.82 compared with 0.75 and 0.77), thus underscoring the benefit of combining multiple modalities in patient outcome prediction.","authors":["Peiying Hua","Andrea Olofson","Faraz Farhadi","Liesbeth Hondelink","Gregory Tsongalis","Konstantin Dragnev","Dagmar Hoegemann Savellano","Arief Suriawinata","Laura Tafe","Saeed Hassanpour"],"pdf_url":"http://arxiv.org/abs/2503.24165","paper_id":"2503.24165","link":"/paper/predicting-targeted-therapy-resistance-in-non","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"predicting-targeted-therapy-resistance-in-non"},{"title":"Crossing Boundaries: Leveraging Semantic Divergences to Explore Cultural Novelty in Cooking Recipes","content":"Novelty modeling and detection is a core topic in Natural Language Processing (NLP), central to numerous tasks such as recommender systems and automatic summarization. It involves identifying pieces of text that deviate in some way from previously known information. However, novelty is also a crucial determinant of the unique perception of relevance and quality of an experience, as it rests upon each individual's understanding of the world. Social factors, particularly cultural background, profoundly influence perceptions of novelty and innovation. Cultural novelty arises from differences in salience and novelty as shaped by the distance between distinct communities. While cultural diversity has garnered increasing attention in artificial intelligence (AI), the lack of robust metrics for quantifying cultural novelty hinders a deeper understanding of these divergences. This gap limits quantifying and understanding cultural differences within computational frameworks. To address this, we propose an interdisciplinary framework that integrates knowledge from sociology and management. Central to our approach is GlobalFusion, a novel dataset comprising 500 dishes and approximately 100,000 cooking recipes capturing cultural adaptation from over 150 countries. By introducing a set of Jensen-Shannon Divergence metrics for novelty, we leverage this dataset to analyze textual divergences when recipes from one community are modified by another with a different cultural background. The results reveal significant correlations between our cultural novelty metrics and established cultural measures based on linguistic, religious, and geographical distances. Our findings highlight the potential of our framework to advance the understanding and measurement of cultural diversity in AI.","authors":["Florian Carichon","Romain Rampa","Golnoosh Farnadi"],"pdf_url":"http://arxiv.org/abs/2503.24027","paper_id":"2503.24027","link":"/paper/crossing-boundaries-leveraging-semantic","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"crossing-boundaries-leveraging-semantic"},{"title":"AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption","content":"Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.","authors":["Florian Bayer","Christian Rathgeb"],"pdf_url":"http://arxiv.org/abs/2503.23949","paper_id":"2503.23949","link":"/paper/amb-fhe-adaptive-multi-biometric-fusion-with","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"amb-fhe-adaptive-multi-biometric-fusion-with"},{"title":"Get the Agents Drunk: Memory Perturbations in Autonomous Agent-based Recommender Systems","content":"Large language model-based agents are increasingly used in recommender systems (Agent4RSs) to achieve personalized behavior modeling. Specifically, Agent4RSs introduces memory mechanisms that enable the agents to autonomously learn and self-evolve from real-world interactions. However, to the best of our knowledge, how robust Agent4RSs are remains unexplored. As such, in this paper, we propose the first work to attack Agent4RSs by perturbing agents' memories, not only to uncover their limitations but also to enhance their security and robustness, ensuring the development of safer and more reliable AI agents. Given the security and privacy concerns, it is more practical to launch attacks under a black-box setting, where the accurate knowledge of the victim models cannot be easily obtained. Moreover, the practical attacks are often stealthy to maximize the impact. To this end, we propose a novel practical attack framework named DrunkAgent. DrunkAgent consists of a generation module, a strategy module, and a surrogate module. The generation module aims to produce effective and coherent adversarial textual triggers, which can be used to achieve attack objectives such as promoting the target items. The strategy module is designed to `get the target agents drunk' so that their memories cannot be effectively updated during the interaction process. As such, the triggers can play the best role. Both of the modules are optimized on the surrogate module to improve the transferability and imperceptibility of the attacks. By identifying and analyzing the vulnerabilities, our work provides critical insights that pave the way for building safer and more resilient Agent4RSs. Extensive experiments across various real-world datasets demonstrate the effectiveness of DrunkAgent.","authors":["Shiyi Yang","Zhibo Hu","Chen Wang","Tong Yu","Xiwei Xu","Liming Zhu","Lina Yao"],"pdf_url":"http://arxiv.org/abs/2503.23804","paper_id":"2503.23804","link":"/paper/get-the-agents-drunk-memory-perturbations-in","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"get-the-agents-drunk-memory-perturbations-in"},{"title":"Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure","content":"Recommender systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposing niche or novel content. Existing bias mitigation methods, such as Inverse Propensity Scoring (IPS) and Off- Policy Correction (OPC), primarily operate at the ranking stage or during training, lacking explicit real-time control over exposure dynamics. In this work, we introduce an exposure- aware retrieval scoring approach, which explicitly models item exposure probability and adjusts retrieval-stage ranking at inference time. Unlike prior work, this method decouples exposure effects from engagement likelihood, enabling controlled trade-offs between fairness and engagement in large-scale recommendation platforms. We validate our approach through online A/B experiments in a real-world video recommendation system, demonstrating a 25% increase in uniquely retrieved items and a 40% reduction in the dominance of over-popular content, all while maintaining overall user engagement levels. Our results establish a scalable, deployable solution for mitigating popularity bias at the retrieval stage, offering a new paradigm for bias-aware personalization.","authors":["Amit Jaspal","Rahul Agarwal"],"pdf_url":"http://arxiv.org/abs/2503.23630","paper_id":"2503.23630","link":"/paper/finding-interest-needle-in-popularity","publication_date":"Mar 31, 2025","raw_publication_date":"2025-03-31","submission_date":"Mar 31, 2025","images":[],"arxiv_comment":"2 pages","journal_ref":null,"code_available":false,"slug":"finding-interest-needle-in-popularity"},{"title":"A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior","content":"This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration, which must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems.","authors":["Nikil Jayasuriya","Deshan Sumanathilaka"],"pdf_url":"http://arxiv.org/abs/2503.23486","paper_id":"2503.23486","link":"/paper/a-systematic-decade-review-of-trip-route","publication_date":"Mar 30, 2025","raw_publication_date":"2025-03-30","submission_date":"Mar 30, 2025","images":[],"arxiv_comment":"6 pages, 2 figures, 1 table","journal_ref":null,"code_available":false,"slug":"a-systematic-decade-review-of-trip-route"},{"title":"Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform","content":"Sequential Recommender Systems (SRS) aim to model sequential behaviors of users to capture their interests which usually evolve over time. Transformer-based SRS have achieved distinguished successes recently. However, studies reveal self-attention mechanism in Transformer-based models is essentially a low-pass filter and ignores high frequency information potentially including meaningful user interest patterns. This motivates us to seek better filtering technologies for SRS, and finally we find Discrete Wavelet Transform (DWT), a famous time-frequency analysis technique from digital signal processing field, can effectively process both low-frequency and high-frequency information. We design an adaptive time-frequency filter with DWT technique, which decomposes user interests into multiple signals with different frequency and time, and can automatically learn weights of these signals. Furthermore, we develop DWTRec, a model for sequential recommendation all based on the adaptive time-frequency filter. Thanks to fast DWT technique, DWTRec has a lower time complexity and space complexity theoretically, and is Proficient in modeling long sequences. Experiments show that our model outperforms state-of-the-art baseline models in datasets with different domains, sparsity levels and average sequence lengths. Especially, our model shows great performance increase in contrast with previous models when the sequence grows longer, which demonstrates another advantage of our model.","authors":["Sheng Lu","Mingxi Ge","Jiuyi Zhang","Wanli Zhu","Guanjin Li","Fangming Gu"],"pdf_url":"http://arxiv.org/abs/2503.23436","paper_id":"2503.23436","link":"/paper/filtering-with-time-frequency-analysis-an","publication_date":"Mar 30, 2025","raw_publication_date":"2025-03-30","submission_date":"Mar 30, 2025","images":[],"arxiv_comment":null,"journal_ref":null,"code_available":false,"slug":"filtering-with-time-frequency-analysis-an"},{"title":"From Content Creation to Citation Inflation: A GenAI Case Study","content":"This paper investigates the presence and impact of questionable, AI-generated academic papers on widely used preprint repositories, with a focus on their role in citation manipulation. Motivated by suspicious patterns observed in publications related to our ongoing research on GenAI-enhanced cybersecurity, we identify clusters of questionable papers and profiles. These papers frequently exhibit minimal technical content, repetitive structure, unverifiable authorship, and mutually reinforcing citation patterns among a recurring set of authors. To assess the feasibility and implications of such practices, we conduct a controlled experiment: generating a fake paper using GenAI, embedding citations to suspected questionable publications, and uploading it to one such repository (ResearchGate). Our findings demonstrate that such papers can bypass platform checks, remain publicly accessible, and contribute to inflating citation metrics like the H-index and i10-index. We present a detailed analysis of the mechanisms involved, highlight systemic weaknesses in content moderation, and offer recommendations for improving platform accountability and preserving academic integrity in the age of GenAI.","authors":["Haitham S. Al-Sinani","Chris J. Mitchell"],"pdf_url":"http://arxiv.org/abs/2503.23414","paper_id":"2503.23414","link":"/paper/from-content-creation-to-citation-inflation-a","publication_date":"Mar 30, 2025","raw_publication_date":"2025-03-30","submission_date":"Mar 30, 2025","images":[],"arxiv_comment":"20 pages","journal_ref":null,"code_available":false,"slug":"from-content-creation-to-citation-inflation-a"}],"total":200,"topicBlurb":"Recommendation is the task of providing personalized suggestions to users based on their preferences and behavior.","similarResults":false,"query":"Recommendation","userHasHiddenBanner":false,"isMobile":false,"currentBrowser":"","canonicalUrl":"https://www.catalyzex.com/s/Recommendation"},"__N_SSP":true},"page":"/search","query":{"query":"Recommendation"},"buildId":"rcP1HS6ompi8ywYpLW-WW","isFallback":false,"isExperimentalCompile":false,"gssp":true,"scriptLoader":[]}</script></body></html>