Introducing prior auxiliary information from the knowledge graph (KG) to assist the user-item graph can improve the comprehensive performance of the recommender system. Many recent studies show that the ensemble properties of hyperbolic spaces fit the scale-free and hierarchical characteristics exhibited in the above two types of graphs well. However, existing hyperbolic methods ignore the consideration of equivariance, thus they cannot generalize symmetric features under given transformations, which seriously limits the capability of the model. Moreover, they cannot balance preserving the heterogeneity and mining the high-order entity information to users across two graphs. To fill these gaps, we propose a rigorously Lorentz group equivariant knowledge-enhanced collaborative filtering model (LECF). Innovatively, we jointly update the attribute embeddings (containing the high-order entity signals from the KG) and hyperbolic embeddings (the distance between hyperbolic embeddings reveals the recommendation tendency) by the LECF layer with Lorentz Equivariant Transformation. Moreover, we propose Hyperbolic Sparse Attention Mechanism to sample the most informative neighbor nodes. Lorentz equivariance is strictly maintained throughout the entire model, and enforcing equivariance is proven necessary experimentally. Extensive experiments on three real-world benchmarks demonstrate that LECF remarkably outperforms state-of-the-art methods.

As we all know that AI(Artificial Intelligence) is one of the growing revolutionary technology in the business world as well as in science. Nowadays many IT companies are investing in AI. Nowadays, it is very difficult for us to live without our smart phones. But how can we make our smartphones smarter? The answer is Artificial Intelligence.

Although Siri and Google Assistant have the ability to schedule meetings on request, they do not have the social awareness to independently prioritise the appointments. A team of researchers has argued that the future of AI calls for the implementation of social intelligence to ensure that the growth of the technology is not stunted by a lack of social skills. The work, 'Artificial Social Intelligence: A Comparative and Holistic View,' is published in CAAI Artificial Intelligence Research. "Artificial Intelligence has changed our society and our daily life," said first author Lifeng Fan, National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI). "What is the next important challenge for AI in the future? We argue that Artificial Social Intelligence (ASI) is the future of AI." ASI includes multiple siloed subfields, including social perception, Theory of Mind, and social interaction.

Suddenly Artificial Intelligence seems like a penetrating force in almost everything in our life. For some time, machines and gadgets seem to be more capable to understand our needs more aptly than ever before. Machines are imitating human imitating human intelligence and rationale behind various tasks and this is what we grossly term as Artificial Intelligence. Thanks to AI your mobile knows when you do not like receiving notifications and accordingly cancels them or mutes them. Along with that, due to AI now the TV knows your preference of channels and only show those you like.

On a rainy Tuesday in San Francisco, Apple executives took the stage in a crowded auditorium to unveil the fifth-generation iPhone. The phone, which looked identical to the previous version, had a new feature that the audience was soon buzzing about: Siri, a virtual assistant. Scott Forstall, then Apple's head of software, pushed an iPhone button to summon Siri and prodded it with questions. At his request, Siri checked the time in Paris ("8:16 p.m.," Siri replied), defined the word "mitosis" ("Cell division in which the nucleus divides into nuclei containing the same number of chromosomes," it said) and pulled up a list of 14 highly rated Greek restaurants, five of them in Palo Alto, Calif. "I've been in the A.I. field for a long time, and this still blows me away," Mr. Forstall said.

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here, we found that the significant proportion of variation in the performances of different CF techniques can be accounted to two RDC only. The two RDC are the number of ratings per user or Information per User (IpU) and the number of ratings per item or Information per Item (IpI). And the performances of CF algorithms are quadratic to IpU (or IpI) for a square URM. The findings of this study are based on seven well-established CF methods and three popular public recommender datasets: 1M MovieLens, 25M MovieLens, and Yahoo! Music Rating datasets

Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased against inactive users. To address the above issues, we propose \textbf{DP-Fair}, a two-stage framework for collaborative filtering based algorithms. Specifically, it combines differential privacy mechanisms with fairness constraints to protect user privacy while ensuring fair recommendations. The experimental results, based on Amazon datasets, and user history logs collected from Etsy, one of the largest e-commerce platforms, demonstrate that our proposed method exhibits superior performance in terms of both overall accuracy and user group fairness on both shallow and deep recommendation models compared to vanilla DP-SGD.

Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that realistically capture the wide array of user pain points. To enable research on some of the more challenging aspects of parsing realistic conversations, we introduce PRESTO, a public dataset of over 550K contextual multilingual conversations between humans and virtual assistants. PRESTO contains a diverse array of challenges that occur in real-world NLU tasks such as disfluencies, code-switching, and revisions. It is the only large scale human generated conversational parsing dataset that provides structured context such as a user's contacts and lists for each example. Our mT5 model based baselines demonstrate that the conversational phenomenon present in PRESTO are challenging to model, which is further pronounced in a low-resource setup.

The impacts of link recommendations on social networks are challenging to evaluate, and so far they have been studied in limited settings. Observational studies are restricted in the kinds of causal questions they can answer and naive A/B tests often lead to biased evaluations due to unaccounted network interference. Furthermore, evaluations in simulation settings are often limited to static network models that do not take into account the potential feedback loops between link recommendation and organic network evolution. To this end, we study the impacts of recommendations on social networks in dynamic settings. Adopting a simulation-based approach, we consider an explicit dynamic formation model -- an extension of the celebrated Jackson-Rogers model -- and investigate how link recommendations affect network evolution over time. Empirically, we find that link recommendations have surprising delayed and indirect effects on the structural properties of networks. Specifically, we find that link recommendations can exhibit considerably different impacts in the immediate term and in the long term. For instance, we observe that friend-of-friend recommendations can have an immediate effect in decreasing degree inequality, but in the long term, they can make the degree distribution substantially more unequal. Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off. We show that, in counterfactual simulations, removing the indirect effects of link recommendations can make the network trend faster toward what it would have been under natural growth dynamics.

With the rise of big data, artificial intelligence (AI), and data mining techniques, group analysis has increasingly become a powerful tool in many applications, ranging from policy-making, direct marketing, education, to healthcare. For example, an important analysis strategy is group profiling, which extracts and describes the characteristics of groups of people [40]; it has been commonly used for customized recommendations to overcome sparse and missing personal data [25]. The same strategy is also used for mining social media, educational, and healthcare data to understand the shared characteristics of online communities or student/patient cohorts [15, 51, 100]. While it may help to support public and private services or product creations that are better tailored to different communities, group profiles resulted from mathematical inference are typically not valid for every individual regarded as a member in the group (this is known as non-distributive group profiles) [40]. The shared group characteristics extracted from data can have social ramifications such as stereotyping, stigmatization, or lead to pernicious consequences in decision making because individuals might be judged by group characteristics they do not posses [24, 56, 58].