More than half of all adults under 30 have used a dating app, but which ones? Tinder is the most popular dating app -- about half U.S. adults who date online say they've used the service. Match and Bumble are the second- and third-most popular choices. Tinder and Bumble were the most frequented by the youngest singles, 18 to 29, while older mainstays Match and eHarmony were biggest with the 50-and-above crowd. Overall, only 35 percent of all online daters reported paying for an app membership or extra features.

Online social media platforms offer access to a vast amount of information, but sifting through the abundance of news can be overwhelming and tiring for readers. personalised recommendation algorithms can help users find information that interests them. However, most existing models rely solely on observations of user behaviour, such as viewing history, ignoring the connections between the news and a user's prior knowledge. This can result in a lack of diverse recommendations for individuals. In this paper, we propose a novel method to address the complex problem of news recommendation. Our approach is based on the idea of dual observation, which involves using a deep neural network with observation mechanisms to identify the main focus of a news article as well as the focus of the user on the article. This is achieved by taking into account the user's belief network, which reflects their personal interests and biases. By considering both the content of the news and the user's perspective, our approach is able to provide more personalised and accurate recommendations. We evaluate the performance of our model on real-world datasets and show that our proposed method outperforms several popular baselines.

Most if not all on-line item-to-item recommendation systems rely on estimation of a distance like measure (rank) of similarity between items. For on-line recommendation systems, time sensitivity of this similarity measure is extremely important. We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The paper briefly summarizes algorithmic developments based on this self-explanatory observation. The basic idea behind this work is patented in a context of online recommendation systems.

When the Ninth Circuit Court of Appeals considered a lawsuit against Google in 2020, Judge Ronald M. Gould stated his view of the tech giant's most significant asset bluntly: "So-called'neutral' algorithms," he wrote, can be "transformed into deadly missiles of destruction by ISIS." According to Gould, it was time to challenge the boundaries of a little snippet of the 1996 Communications Decency Act known as Section 230, which protects online platforms from liability for the things their users post. The plaintiffs in this case, the family of a young woman who was killed during a 2015 Islamic State attack in Paris, alleged that Google had violated the Anti-terrorism Act by allowing YouTube's recommendation system to promote terrorist content. The algorithms that amplified ISIS videos were a danger in and of themselves, they argued. Gould was in the minority, and the case was decided in Google's favor.

But another big issue is at stake that has received much less attention: depending on the outcome of the case, individual users of sites may suddenly be liable for run-of-the-mill content moderation. Many sites rely on users for community moderation to edit, shape, remove, and promote other users' content online--think Reddit's upvote, or changes to a Wikipedia page. What might happen if those users were forced to take on legal risk every time they made a content decision? In short, the court could change Section 230 in ways that won't just impact big platforms; smaller sites like Reddit and Wikipedia that rely on community moderation will be hit too, warns Emma Llansó, director of the Center for Democracy and Technology's Free Expression Project. "It would be an enormous loss to online speech communities if suddenly it got really risky for mods themselves to do their work," she says.

Visualization Recommendation Systems (VRS) are a novel and challenging field of study, whose aim is to automatically generate insightful visualizations from data, to support non-expert users in the process of information discovery. Despite its enormous application potential in the era of big data, progress in this area of research is being held back by several obstacles among which are the absence of standardized datasets to train recommendation algorithms, and the difficulty in defining quantitative criteria to assess the effectiveness of the generated plots. In this paper, we aim not only to summarize the state-of-the-art of VRS, but also to outline promising future research directions.

How can many people (who may disagree) come together to answer a question or make a decision? "Collective response systems" are a type of generative collective intelligence (CI) facilitation process meant to address this challenge. They enable a form of "generative voting", where both the votes, and the choices of what to vote on, are provided by the group. Such systems overcome the traditional limitations of polling, town halls, standard voting, referendums, etc. The generative CI outputs of collective response systems can also be chained together into iterative "collective dialogues", analogously to some kinds of generative AI. Technical advances across domains including recommender systems, language models, and human-computer interaction have led to the development of innovative and scalable collective response systems. For example, Polis has been used around the world to support policy-making at different levels of government, and Remesh has been used by the UN to understand the challenges and needs of ordinary people across war-torn countries. This paper aims to develop a shared language by defining the structure, processes, properties, and principles of such systems. Collective response systems allow non-confrontational exploration of divisive issues, help identify common ground, and elicit insights from those closest to the issues. As a result, they can help overcome gridlock around conflict and governance challenges, increase trust, and develop mandates. Continued progress toward their development and adoption could help revitalize democracies, reimagine corporate governance, transform conflict, and govern powerful AI systems -- both as a complement to deeper deliberative democratic processes and as an option where deeper processes are not applicable or possible.

After a few years of staying mostly under the radar, Instagram co-founders Kevin Systrom and Mike Krieger are back with a new project. It's an app called Artifact, a name Systrom told Platformer's Casey Newton is designed to evoke the project's three tenants: "articles, facts and artificial intelligence." In short, it's a news aggregation app driven by a TikTok-like recommendation algorithm. When you first launch Artifact, you'll see a central feed populated by stories from publications like The New York Times. As you read more articles, the app will begin personalizing your feed.

Machine learning is an advanced field that incorporates many aspects of mathematics, computer science, and coding. A career in machine learning typically requires a Master's of Science degree. The education and training involved in machine learning can require intense dedication, depth of knowledge, and attention to detail. You can get started with machine learning by learning coding languages, practicing fine-tuning algorithms, and paying close attention to artificial intelligence applications for products and services. Everything from the technology of a Tesla vehicle, Netflix's recommendation algorithms, c or speech-to-text recognition on your iPhone represents an innovation in machine learning. You can find information about machine learning from a breadth of free, accessible resources.

Recommender systems are a class of online reinforcement learning algorithms that interact sequentially with an environment suggesting relevant items to a user. During the last decade, there has been an increasing interest in online recommendation techniques due to the importance of advertisement recommendation for e-commerce websites or the rise of movies and music streaming platforms [1, 2]. Among different settings for recommender systems, in this work, we focus on the contextual bandit framework applied to the recommendation of quantum data. The contextual bandit problem is a variant of the multi-armed bandit problem where a learner at each round receives a context and given a set of actions (also called actions) has to decide the best action using the context information. After selecting an action the learner will receive a reward and for the next rounds, they will use the previous information of contexts and rewards in order to make their future choices.