A huge data breach has leaked over 50,000 profiles from the'Gay Daddy' dating app, cybersecurity researchers have discovered. The exposed data contains extremely sensitive information including users' names, ages, location data and HIV status. According to experts from Cybernews, the exposed database also contains over 124,000 private messages and photos – many of which are explicit. While the app markets itself as a'private and anonymous community', researchers say the information could be accessed by anyone with'basic technical knowledge'. Researchers say the app's'devastating' security failure puts its users at serious risk of blackmail, exploitation and even physical harm.

NN, MM YYYY 1 SCRec: A Scalable Computational Storage System with Statistical Sharding and Tensor-train Decomposition for Recommendation Models Jinho Y ang, Graduate Student Member, IEEE, Ji-Hoon Kim, Graduate Student Member, IEEE, Joo-Y oung Kim, Senior Member, IEEE, Abstract --Deep Learning Recommendation Models (DLRMs) play a crucial role in delivering personalized content across web applications such as social networking and video streaming. However, with improvements in performance, the parameter size of DLRMs has grown to terabyte (TB) scales, accompanied by memory bandwidth demands exceeding TB/s levels. Furthermore, the workload intensity within the model varies based on the target mechanism, making it difficult to build an optimized recommendation system. In this paper, we propose SCRec, a scalable computational storage recommendation system that can handle TB-scale industrial DLRMs while guaranteeing high bandwidth requirements. SCRec utilizes a software framework that features a mixed-integer programming (MIP)-based cost model, efficiently fetching data based on data access patterns and adaptively configuring memory-centric and compute-centric cores. Additionally, SCRec integrates hardware acceleration cores to enhance DLRM computations, particularly allowing for the high-performance reconstruction of approximated embedding vectors from extremely compressed tensor-train (TT) format. By combining its software framework and hardware accelerators, while eliminating data communication overhead by being implemented on a single server, SCRec achieves substantial improvements in DLRM inference performance. It delivers up to 55.77 speedup compared to a CPU-DRAM system with no loss in accuracy and up to 13.35 energy efficiency gains over a multi-GPU system. I NTRODUCTION R RECOMMENDA TION systems are widely used in social network services and video streaming platforms to provide personalized and preferred content to consumers as described in Fig.1. They are also employed in search engines to offer differentiated search services [1]-[5]. For example, more than 80% of Meta's data center resources are allocated to recommendation system inference, while over 50% are utilized for training these systems [6]. Traditional recommendation systems relied on collaborative filtering techniques, such as content filtering using matrix factorization [7]-[10]. However, with advancements in deep neural networks (DNNs), deep learning recommendation models (DLRMs) that combine embedding tables (EMBs) and This work was supported by Samsung Electronics Co., Ltd.. Manuscript received MM dd, yyyy; revised MM dd, yyyy. These models are widely adopted in data centers, with recent focuses on both software-level and hardware-level optimizations [11]- [17]. This combination has demonstrated superior recommendation performance, making DLRM the industry standard in recommendation systems.

Cybersecurity researchers have issued an urgent warning as almost 1.5 million private photos from dating apps are exposed. Affected apps include the kink dating sites BDSM People and CHICA, as well as LGBT dating services PINK, BRISH, and TRANSLOVE - all of which were developed by M.A.D Mobile. The leaked files include photos used for verification, photos removed by app moderators, and photos sent in direct messages between users - many of which were explicit. These sensitive snaps were being stored online without password protection, meaning anyone with the link could view and download them. Researchers from Cybernews, who discovered the vulnerability, say this easily exploited security flaw put up to 900,000 users at risk of further hacks or extortion.

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.

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.

Mr Nazarovas said the discovery of unprotected sensitive material comes with a significant risk for the platforms' users. Malicious hackers could have found the images and extorted individuals. There is also a risk to those who live in countries hostile to LGBT people. None of the text content of private messages was found to be stored in this way and the images are not labelled with user names or real names, which would make crafting targeted attacks at users more complex. In an email M.A.D Mobile said it was grateful to the researcher for uncovering the vulnerability in the apps to prevent a data breach from occurring.

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.

Generative recommendation (GR) has become a powerful paradigm in recommendation systems that implicitly links modality and semantics to item representation, in contrast to previous methods that relied on non-semantic item identifiers in autoregressive models. However, previous research has predominantly treated modalities in isolation, typically assuming item content is unimodal (usually text). We argue that this is a significant limitation given the rich, multimodal nature of real-world data and the potential sensitivity of GR models to modality choices and usage. Our work aims to explore the critical problem of Multimodal Generative Recommendation (MGR), highlighting the importance of modality choices in GR nframeworks. We reveal that GR models are particularly sensitive to different modalities and examine the challenges in achieving effective GR when multiple modalities are available. By evaluating design strategies for effectively leveraging multiple modalities, we identify key challenges and introduce MGR-LF++, an enhanced late fusion framework that employs contrastive modality alignment and special tokens to denote different modalities, achieving a performance improvement of over 20% compared to single-modality alternatives.

--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--that must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems. Navigation systems have evolved significantly from early cartographic solutions to the sophisticated, real-time route planners we rely on today. With the rise of urbanization and the increasing complexity of transportation networks, modern navigation tools have become integral to our daily lives.

Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains, visually driven categories such as fashion or home decor potentially require detailed visual information related to color, style, or design. To address this challenge, we propose LaViC (Large Vision-Language Conversational Recommendation Framework), a novel approach that integrates compact image representations into dialogue-based recommendation systems. LaViC leverages a large vision-language model in a two-stage process: (1) visual knowledge self-distillation, which condenses product images from hundreds of tokens into a small set of visual tokens in a self-distillation manner, significantly reducing computational overhead, and (2) recommendation prompt tuning, which enables the model to incorporate both dialogue context and distilled visual tokens, providing a unified mechanism for capturing textual and visual features. To support rigorous evaluation of visually-aware conversational recommendation, we construct a new dataset by aligning Reddit conversations with Amazon product listings across multiple visually oriented categories (e.g., fashion, beauty, and home). This dataset covers realistic user queries and product appearances in domains where visual details are crucial. Extensive experiments demonstrate that LaViC significantly outperforms text-only conversational recommendation methods and open-source vision-language baselines. Moreover, LaViC achieves competitive or superior accuracy compared to prominent proprietary baselines (e.g., GPT-3.5-turbo, GPT-4o-mini, and GPT-4o), demonstrating the necessity of explicitly using visual data for capturing product attributes and showing the effectiveness of our vision-language integration. Our code and dataset are available at https://github.com/jeon185/LaViC.