In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel benchmarking methodology to facilitate a fair and robust comparison of RecSys algorithms, thereby advancing evaluation practices. By utilizing a diverse set of $30$ open datasets, including two introduced in this work, and evaluating $11$ collaborative filtering algorithms across $9$ metrics, we critically examine the influence of dataset characteristics on algorithm performance. We further investigate the feasibility of aggregating outcomes from multiple datasets into a unified ranking. Through rigorous experimental analysis, we validate the reliability of our methodology under the variability of datasets, offering a benchmarking strategy that balances quality and computational demands. This methodology enables a fair yet effective means of evaluating RecSys algorithms, providing valuable guidance for future research endeavors.

For a while, it seemed like the only place to meet potential partners was through an app--Tinder, Hinge, Bumble, etc. But as the apps are trying to monetize their matchmaking--and some users now with a whole decade of striking out under their belts--old-fashioned meet-cutes-in-bars or, say, debutante balls look more and more appealing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

When I met my husband, who happens to be white, he told me that he was always seeing women with blonde hair on Tinder and he's not really into blondes. No matter how many times he had swiped left on blondes, the algorithms were always recommending them to him, presumably because pop culture dictates that white men prefer blondes. Luckily for us, the algorithms' tendency to stack blonde women in his swipe deck worked out in our favor because I'm a black woman who, at the time, had blonde hair. In nearly 10 years of swiping through profiles on Tinder, Bumble, Hinge, and OkCupid, I learned that dating apps can provide pathways for finding friendship, adventure, romance, and sometimes, love. But there was one aspect of dating app culture that I couldn't ignore because it was often the first thing matches wanted to talk about: race.

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.