'The Big Weekend Show' co-hosts discuss Tinder user traffic peaking during'Dating Sunday.' A judge has dismissed kidnapping and conspiracy charges filed against five Massachusetts college students accused of luring a man to their campus in a "Catch a Predator"-style scheme using a dating app. A Worcester District Court judge dismissed the charges against Kelsey Brainard, Isabella Trudeau, Joaquin Smith, Kevin Carroll and Easton Randall on Tuesday. The decision came after lawyers for the teenage Assumption University students claimed prosecutors lacked probable cause and filed motions to dismiss last month. Information regarding the status of a sixth student, charged as a juvenile, was not immediately available.

However, these practices can lead to significant privacy risks, including data exploitation Barocas and Nissenbaum [2014], re-identification threats Narayanan and Shmatikov [2008], and surveillance concerns Lyon [2014]. To address these issues, several privacy-preserving techniques have been proposed, including differential privacy McSherry and Mironov [2009], federated learning Ammad-Ud-Din et al. [2019], homomorphic encryption Kim et al. [2016], privacy-preserving matrix factorization Hua and Xiong [2015], and K-anonymity Polat and Du [2005]. Despite their potential, these methods often face challenges such as reduced utility, computational complexity, and communication overhead. In this work, we explore a privacy-preserving recommendation system where user queries are protected using differential privacy within the local trust model Bebensee [2019], with a focus on balancing the trade-offs between utility and privacy. In the local trust model, user queries and user features are changed from the original to preserve privacy (typically by adding noise), which can lead to less accurate results from the server. To mitigate this issue, Goel et al. [2024] introduced the concept of multi-selection, where the server returns multiple results, allowing the user to select the most relevant one without disclosing its Supported by NSF awards CCF-2113798 and IIS-2402823. 1 arXiv:2504.07403v1

Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. Although Transformer-based models have proven effective for sequential recommendation, the complexity of the self-attention module in Transformers scales quadratically with the sequence length. Controlling model complexity is essential for large-scale recommendation systems, as these systems may need to handle billion-scale vocabularies that evolve continuously, as well as user behavior sequences that can exceed tens of thousands in length. In this paper, we propose a novel multi-head latent Mamba architecture, which employs multiple low-dimensional Mamba layers and fully connected layers coupled with positional encoding to simultaneously capture historical and item information within each latent subspace. Our proposed method not only enables scaling up to large-scale parameters but also extends to multi-domain recommendation by integrating and fine-tuning LLMs. Through extensive experiments on public datasets, we demonstrate how Hydra effectively addresses the effectiveness-efficiency dilemma, outperforming state-of-the-art sequential recommendation baselines with significantly fewer parameters and reduced training time.

The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.

User profiling, the practice of collecting user information for personalized recommendations, has become widespread, driving progress in technology. However, this growth poses a threat to user privacy, as devices often collect sensitive data without their owners' awareness. This article aims to consolidate knowledge on user profiling, exploring various approaches and associated challenges. Through the lens of two companies sharing user data and an analysis of 18 popular Android applications in India across various categories, including $\textit{Social, Education, Entertainment, Travel, Shopping and Others}$, the article unveils privacy vulnerabilities. Further, the article propose an enhanced machine learning framework, employing decision trees and neural networks, that improves state-of-the-art classifiers in detecting personal information exposure. Leveraging the XAI (explainable artificial intelligence) algorithm LIME (Local Interpretable Model-agnostic Explanations), it enhances interpretability, crucial for reliably identifying sensitive data. Results demonstrate a noteworthy performance boost, achieving a $75.01\%$ accuracy with a reduced training time of $3.62$ seconds for neural networks. Concluding, the paper suggests research directions to strengthen digital security measures.

--Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experiences. Current research efforts predominantly concentrate on adapting traditional recommendation architectures to federated environments, optimizing communication efficiency, and mitigating security vulnerabilities. However, user personalization modeling, which is essential for capturing heterogeneous preferences in this decentralized and non-IID data setting, remains underexplored. This survey addresses this gap by systematically exploring personalization in FedRecSys, charting its evolution from centralized paradigms to federated-specific innovations. We establish a foundational definition of person-alization in a federated setting, emphasizing personalized models as a critical solution for capturing fine-grained user preferences. The work critically examines the technical hurdles of building personalized FedRecSys and synthesizes promising methodologies to meet these challenges. As the first consolidated study in this domain, this survey serves as both a technical reference and a catalyst for advancing personalized FedRecSys research. A. Motivation Federated recommender systems (FedRecSys) [1]-[6] have burgeoned as a remarkable paradigm to promote privacy-preserving recommendation services. Besides, the distributed optimization pattern enables service providers to effectively harness the vast computational resources of various devices. This balance between performance and privacy protection makes FedRecSys an attractive research avenue with significant potential for edge AI development. Current research in FedRecSys primarily derives from the perspectives of RecSys and FL views. Chunxu Zhang, Zijian Zhang and Bo Y ang are with the College of Computer Science and Technology, Jilin University, Jilin, China (e-mail: zhangchunxu@jlu.edu.cn, Guodong Long and Zhiwei li are with the Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia (e-mail: guodong.long@uts.edu.au, Honglei Zhang is with the School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China (e-mail: hon-glei.zhang@bjtu.edu.cn). Qiang Y ang is Professor Emeritus at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, and the Chief AI Officer of WeBank, Shenzhen, China (e-mail: qyang@cse.ust.hk). Personalization technique comparison in centralized and federated RecSys. The colorful module denotes the user-specific parameters and the gray module represents the user-shared parameters. FL's ability to collaboratively train multiple models across different devices naturally supports the development of personalized models, making it easier to tailor recommendations to individual user needs.

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities between images and items. Consequently, items often exhibit distinct anisotropic and directional structures that are less prevalent in images. However, the traditional forward diffusion process continuously adds isotropic Gaussian noise, causing anisotropic signals to degrade into noise, which impairs the semantically meaningful representations in recommender systems. Inspired by the advancements in hyperbolic spaces, we propose a novel \textit{\textbf{H}yperbolic} \textit{\textbf{D}iffusion} \textit{\textbf{R}ecommender} \textit{\textbf{M}odel} (named HDRM). Unlike existing directional diffusion methods based on Euclidean space, the intrinsic non-Euclidean structure of hyperbolic space makes it particularly well-adapted for handling anisotropic diffusion processes. In particular, we begin by formulating concepts to characterize latent directed diffusion processes within a geometrically grounded hyperbolic space. Subsequently, we propose a novel hyperbolic latent diffusion process specifically tailored for users and items. Drawing upon the natural geometric attributes of hyperbolic spaces, we impose structural restrictions on the space to enhance hyperbolic diffusion propagation, thereby ensuring the preservation of the intrinsic topology of user-item graphs. Extensive experiments on three benchmark datasets demonstrate the effectiveness of HDRM.

From popular apps like Tinder, Bumble, and Hinge to niche platforms like Singles with Food Allergies, Ugly Schmuks, and Mullet Passions, it seems there's now a dating app for everyone. But regardless of your app choice, one thing's for sure - you should never send these messages. Experts from FindingTheOne.com surveyed 1,000 singletons about the messages they hate to receive online. Topping the list is a basic'hey' or'hi', which a whopping 78 per cent of users said they despise. Acording to the survey, it's also wise to avoid cheesy pickup lines, as these could put off 59 per cent of singletons.

As telecommunication service providers shifting their focus to analyzing user behavior for package design and marketing interventions, a critical challenge lies in developing a unified, end-to-end framework capable of modeling long-term and periodic user behavior sequences with diverse time granularities, multi-modal data inputs, and heterogeneous labels. This paper introduces GTS-LUM, a novel user behavior model that redefines modeling paradigms in telecommunication settings. GTS-LUM adopts a (multi-modal) encoder-adapter-LLM decoder architecture, enhanced with several telecom-specific innovations. Specifically, the model incorporates an advanced timestamp processing method to handle varying time granularities. It also supports multi-modal data inputs -- including structured tables and behavior co-occurrence graphs -- and aligns these with semantic information extracted by a tokenizer using a Q-former structure. Additionally, GTS-LUM integrates a front-placed target-aware mechanism to highlight historical behaviors most relevant to the target. Extensive experiments on industrial dataset validate the effectiveness of this end-to-end framework and also demonstrate that GTS-LUM outperforms LLM4Rec approaches which are popular in recommendation systems, offering an effective and generalizing solution for user behavior modeling in telecommunications.

As social robots and other artificial agents become more conversationally capable, it is important to understand whether the content and meaning of self-disclosure towards these agents changes depending on the agent's embodiment. In this study, we analysed conversational data from three controlled experiments in which participants self-disclosed to a human, a humanoid social robot, and a disembodied conversational agent. Using sentence embeddings and clustering, we identified themes in participants' disclosures, which were then labelled and explained by a large language model. We subsequently assessed whether these themes and the underlying semantic structure of the disclosures varied by agent embodiment. Our findings reveal strong consistency: thematic distributions did not significantly differ across embodiments, and semantic similarity analyses showed that disclosures were expressed in highly comparable ways. These results suggest that while embodiment may influence human behaviour in human-robot and human-agent interactions, people tend to maintain a consistent thematic focus and semantic structure in their disclosures, whether speaking to humans or artificial interlocutors.