Dating apps promised to make finding love easier. For many users, though, they've just made it more exhausting. Swiping, ghosting, and endless conversations that rarely materialize into real-life dates have left people burned out and disillusioned. A cultural shift is underway as singles ditch the apps in favor of real-world connections. WIRED went looking for love and found that modern romance is a web of scams, AI boyfriends, and Tinder burnout.

Since it first appeared with the launch of match.com Around 10% of heterosexual people and 24% of LGBT people have met their long-term partner online, according to Pew Research Center. But evidence suggests that young people are switching off dating apps, with the UK's top 10 seeing a fall of nearly 16%, according to a report published by Ofcom in November 2024. Tinder lost 594,000 users, while Hinge dropped by 131,000, Bumble by 368,000 and Grindr by 11,000, the report said (a Grindr spokesperson said they were "not familiar with this study's source data" and that their UK users "continue to rise year over year"). According to a 2023 Axios study of US college students and other Gen Zers, 79% said they were forgoing regular dating app usage.

Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.

Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behaviour. However, such models for sequential data usually process a single sequence, ignoring context from other relevant ones, even in domains with rapidly changing external environments like finance or misguiding the prediction for a user with no recent events. We are the first to propose a method that aggregates information from multiple user representations augmenting a specific user one for a scenario of multiple co-occurring event sequences. Our study considers diverse aggregation approaches, ranging from simple pooling techniques to trainable attention-based approaches, especially Kernel attention aggregation, that can highlight more complex information flow from other users. The proposed method operates atop an existing encoder and supports its efficient fine-tuning. Across considered datasets of financial transactions and downstream tasks, Kernel attention improves ROC AUC scores, both with and without fine-tuning, while mean pooling yields a smaller but still significant gain.

Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations.

Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ranking utilizes lightweight models to perform a preliminary selection from massive retrieved candidates. However, recent works focus solely on improving consistency with ranking, relying exclusively on downstream stages. Since downstream input is derived from the pre-ranking output, they will exacerbate the sample selection bias (SSB) issue and Matthew effect, leading to sub-optimal results. To address the limitation, we propose a novel Hybrid Cross-Stage Coordination Pre-ranking model (HCCP) to integrate information from upstream (retrieval) and downstream (ranking, re-ranking) stages. Specifically, cross-stage coordination refers to the pre-ranking's adaptability to the entire stream and the role of serving as a more effective bridge between upstream and downstream. HCCP consists of Hybrid Sample Construction and Hybrid Objective Optimization. Hybrid sample construction captures multi-level unexposed data from the entire stream and rearranges them to become the optimal guiding "ground truth" for pre-ranking learning. Hybrid objective optimization contains the joint optimization of consistency and long-tail precision through our proposed Margin InfoNCE loss. It is specifically designed to learn from such hybrid unexposed samples, improving the overall performance and mitigating the SSB issue. The appendix describes a proof of the efficacy of the proposed loss in selecting potential positives. Extensive offline and online experiments indicate that HCCP outperforms SOTA methods by improving cross-stage coordination. It contributes up to 14.9% UCVR and 1.3% UCTR in the JD E-commerce recommendation system. Concerning code privacy, we provide a pseudocode for reference.

The past decade has brought the world a lot of political and economic chaos, but in its defense, that same span of time has also given us the Paddington Bear movies. With those two London-set adventures, a mix of animation (Paddington) and live action (everyone else), director Paul King created a loopy world all his own, as cozy and visually pleasing as a dollhouse. The Paddington films were also refreshingly gentle, with moral messages that emerged not from preachy dialogue but from their ursine protagonist's unassuming goodness. And Ben Whishaw's voice performance as the unfailingly polite, naively bumbling bear is one of the all-time great matches between actor and animated character, up there with Tom Hanks' Woody in the Toy Story films: Whishaw quite simply is Paddington, and the completeness and believability of his characterization would have set the films apart even without their droll scripts and all-in supporting casts. The third film in the series, Paddington in Peru, ran a high risk of becoming a shark-jumping sequel, with King and his co-writers now replaced by first-time feature director Dougal Wilson and a new writing team consisting of Mark Burton, Jon Foster, and James Lamont.

Dating apps have evolved a lot over the years, with apps dedicated to any romantic niche–dog lovers, astrology heads, and big, bushy beards. Despite the seemingly endless options of dating platforms, the industry seems to be at a low. So this week, we talk about the current state of dating apps and what it means for those looking for love (or something like it). Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link.

A new investigation from The Markup claims the parent company of Tinder, Hinge, OKCupid and other dating apps turns a blind eye to allegedly abusive users on its platforms. The 18-month investigation found instances in which users who were repeatedly reported for drugging or assaulting their dates remained on the apps. One such case involves a Colorado-based cardiologist named Stephen Matthews. Over several years, multiple women on Match's platforms reported him for drugging or raping them. Despite these reports, his Tinder profile was at one point given Standout status, reserved for popular profiles and often requiring in-app currency to interact with.

In her 2012 book, Addiction by Design, anthropologist Natasha Dow Schüll lays out the different technological mechanisms casinos employ to keep people gambling. From the architecture of buildings and placement of ATMs to the design of casino carpets--all of it exemplifies strategic calculation. As a blurb for a gambling trade show once put it, the various elements making up the modern gambling experience are "symphonies of individual technologies" that come together to "create a single experience," calibrated in a way to keep people playing, to maximize "time on device." "There's something very similar about the mechanisms that are built into dating apps, especially with swiping," she says. "Swiping left and right--it's almost like a horizontal slot machine. You really don't know what you're going to get."