AI girls, or AI-powered virtual assistants, do not have physical appearance or emotions like human girls. However, here are some things that are considered "beautiful" or advantageous about AI girls in a virtual or technological context: Efficiency: AI girls can perform tasks quickly and accurately, without getting tired or making mistakes. Customization: AI girls can be programmed to respond to specific commands or perform specific functions, making them highly customizable to meet different needs. Availability: AI girls are available 24/7, without breaks or time off, making them highly convenient for users who need assistance at any time. Language abilities: AI girls can understand and respond to multiple languages, making them accessible to a wide range of users. Cost-effectiveness: In many cases, AI girls are more cost-effective than hiring human workers, as they do not require salaries, benefits, or time off.

If you have an Amazon Echo device in your home, you most likely use it for everyday uses, such as listening to music or checking the news. However, there are a range of interesting things Alexa can also do. Once you know what to ask, you can put your Alexa to the test and ask her to supply you with hilarious jokes, pop culture references, trivia, and much more. These hidden features will certainly not leave users disappointed and are worth giving a go. Here is a list of 15 questions you can ask Alexa to lighten the mood or to tackle your boredom.

In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices and the transmission of users' data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of the proposed approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. Therefore, the standard train-validation-test evaluation procedure is used. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices, and evaluates our model in this case. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.

Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL.

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.