If you're single this Valentine's Day, you might be tempted to download a dating app. But knowing where to start can be a daunting process. From Tinder to Plenty of Fish - and even Singles With Food Allergies - there are thousands of apps to choose from. To help you get started, Ofcom has released new data detailing the most popular platforms among different age groups in Britain. So, whether you're a Gen Z, a Millennial, or even a Silver Fox, use our interactive tool to find out where your peers are looking for love.

Dating expert Cher Gopman shares how to find love in the new year on'Fox & Friends.' Being single on Valentine's Day can be annoying for some people -- but so can dating. And at a time when online dating is the new norm, experts say there are easier ways to drum up love without swiping for it. Dr. Susan Albersis, a psychologist at Cleveland Clinic in Ohio, told Fox News Digital in a statement that online dating is a "double-edged sword." "On one hand, it creates wonderful connections," she said. "The downside is that it can often bruise your self-esteem."

Candice Lim is joined by dating culture researcher Lakshmi Rengarajan and culture writer Kate Lindsay to discuss the past, present and future of dating apps. Online dating has been around since the days of dial-up. But apps like Tinder disrupted the market and changed the way we've dated for the past decade. Recently, there's been several trends emerging, from Gen-Z abandoning the apps to baby boomers finding love later in life. So are we witnessing the death of dating apps or have they integrated themselves so deeply into our lives that we can't live without them?

Nobody can blame you for wanting to continue driving your older car that you've managed to pay off. But you're missing out on convenience and safety features that come standard in newer cars, like backup cameras and hands-free navigation. Fortunately, you can upgrade your older car with this 9″ Wireless Car Display. This touchscreen display is compatible with both Apple CarPlay and Android Auto and even includes a 1080p backup camera. Not only can you enjoy the benefits of accessing your contacts, music, and Maps by touch or activating Siri or Google Assistant voice assistants, but you can also add a backup camera to your car for additional safety.

Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility.

The development of large vision-language models (LVLMs) offers the potential to address challenges faced by traditional multimodal recommendations thanks to their proficient understanding of static images and textual dynamics. However, the application of LVLMs in this field is still limited due to the following complexities: First, LVLMs lack user preference knowledge as they are trained from vast general datasets. Second, LVLMs suffer setbacks in addressing multiple image dynamics in scenarios involving discrete, noisy, and redundant image sequences. To overcome these issues, we propose the novel reasoning scheme named Rec-GPT4V: Visual-Summary Thought (VST) of leveraging large vision-language models for multimodal recommendation. We utilize user history as in-context user preferences to address the first challenge. Next, we prompt LVLMs to generate item image summaries and utilize image comprehension in natural language space combined with item titles to query the user preferences over candidate items. We conduct comprehensive experiments across four datasets with three LVLMs: GPT4-V, LLaVa-7b, and LLaVa-13b. The numerical results indicate the efficacy of VST.

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency. However, these methods failed to consider the importance of various interactions that reflect unique user/item characteristics and failed to utilize user and item high-order neighborhood information to model user preference, thus leading to sub-optimal performance. To address the above issues, we propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter that work in a successive manner, to capture both unique and common user/item characteristics to more accurately model user preference. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood, to fully utilize high-order neighborhood information of users/items for more accurate user preference modeling. Finally, we combine these two modules via a linear model to further improve recommendation accuracy. Extensive experiments on six public datasets demonstrate the superiority of our method from the perspectives of prediction accuracy and training efficiency compared with state-of-the-art GCN-based recommendation methods and GSP-based recommendation methods.

The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help students to choose courses that suit to their personal interests and their academic performance. This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF) using multiple criteria related both to student and course information to recommend the most suitable courses to the students. A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration which include both the most relevant criteria and the configuration of the rest of parameters. The experimental study has used real information of Computer Science Degree of University of Cordoba (Spain) including information gathered from students during three academic years, counting on 2500 entries of 95 students and 63 courses. Experimental results show a study of the most relevant criteria for the course recommendation, the importance of using a hybrid model that combines both student information and course information to increase the reliability of the recommendations as well as an excellent performance compared to previous models.

This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of Recommendation Engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.

Personalized news recommendation is important for users to find interesting news from massive information. As a heated topic that has wide applications in the industry, it has been extensively studied over decades and has made huge progress. In this paper, we discuss the topic of news recommendation. We implement an attention based model and demonstrate the great performance-boosting modern deep learning techniques bring in. The following chapter will be structured as follow: in chapter 2, we will briefly introduce the scenario of news recommendation. In Chapter 3, we discuss the details of the implementation of our model. In Chapter 4, we introduce the dataset we use. Chapter 5 will be a recap and conclusion Figure.1.