New York – Dating apps offer a snapshot about a person's life, but in the space of a few weeks, a surprising health issue has emerged as a dealmaker or heartbreaker: Have you had the coronavirus vaccine? Some are bragging they have gotten the shot in order to better their chances, while others are using it to justify what one singleton described as "the most 2021 rejection ever." But can you trust every lonely heart who claims they've been inoculated against COVID-19? Samantha Yammine, a scientist who often talks on Twitter about health issues, says she's received messages about "dudes on dating apps claiming they're'totally safe for close contact' because they have received the vaccine." Of course, most young people using dating apps are not in vaccination priority groups at the front of the line, so some see having gotten the shot as a sort of golden ticket for hooking up.

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.

Online gaming is a multi-billion-dollar industry, which is growing faster than ever before. Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M$^3$Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M$^3$Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M$^3$Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.

There are many sources that give similar answers to the question, 'What is AI?' By the 1950s, there were many scientists, mathematicians, and philosophers that were looking into the concept of Artificial Intelligence. One such person was Alan Turing, who to this day is considered by many to be the Father of Artificial Intelligence. He formed the idea and mathematical and logical reasoning behind the concept of machine intelligence wherein machines and computers would be able to replicate the behavior of humans and their intelligence. His paper Computing Machinery and Intelligence outlines his logic for the start of artificial intelligence. Fast forward 70 years into the future and we are now in a world where computers are able to converse with humans, albeit with limitations, but this is the progress we see as our world progresses to a more sophisticated AI. "The design and development of computer systems that have the knowledge and skills required to perform the tasks which usually require human intelligence to undertake" –AILab "The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings."

There's been such a rise in intentional dating, with more people looking for something serious because they're reminded of the value of human connection. And, of course, people have so much more free time.

Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context, recommendations are determined, for example, on the basis of analyzing the preferences of similar users. In contrast to simple items which can be enumerated in an item catalog, complex items have to be represented on the basis of variability models (e.g., feature models) since a complete enumeration of all possible configurations is infeasible and would trigger significant performance issues. In this paper, we give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques in feature modeling and configuration. In this context, we give examples of the application of recommender systems and machine learning and discuss future research issues.

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods.

Whether you're looking for love or ways to celebrate your loved one, technology is playing an increasingly important role – especially during a pandemic. After all, many of us are forced to remain socially distant for the time being. Valentine's Day might be celebrated at home this year, as opposed to dining in a restaurant, and florists may sell more bouquets to online customers instead of in-store shoppers. As the expression goes, there's an app for that. Interestingly, even online dating apps aren't just used to find a mate over the internet, but quite literally to date online – until it's safe to meet in person.

Unfortunately, scams trying to steal your heart and money are, too. As Valentine's Day nears, potential scammers are attempting to take advantage, focused on stealing personal information or money. Whether you're looking for love on social networks or dating sites or looking to buy a special gift for your loved one, scammers are lurking to trick you. This season in particular, as many Americans remain homebound due to COVID-19 outbreaks, the number of scams related to romance or Valentine's Day is on the rise. Lynette Owens, global director of internet safety at Trend Micro, said scams related to romance are up 20% over last year, caused by the "double whammy" of people staying online more due to the pandemic and increased isolation.

The listing of Bumble, which also owns Badoo, makes a billionaire of 31-year-old founder Whitney Wolfe Herd.