'Swiping left' and'swiping right' have become ubiquitous with whether we find someone attractive or not, all thanks to the rise of dating apps. The likes of Tinder, Bumble and Hinge have made online dating pocket-sized, and singletons can whip out their phone wherever they are to search for a partner. But this accessibility has arguably made it more difficult than ever to stand out from the crowd, with an estimated 300 million people are currently using dating apps worldwide. Fortunately, experts are here to help the lonely hearts, and have worked tirelessly over the years to find the secret formula for success in online dating. Studies have shown that having a dog in your photos or an Apple product increase your chance of getting a match.

In this day and age of technological advancements, people are looking for solutions to automate regular and repetitive tasks as much as possible. As such, the development of artificial intelligence algorithms has come a long way to help in automation and reduce human labor. It would give us humans enough time to focus on the development of our own skills and pursue our dreams and aspirations. With the potential artificial intelligence has shown, many industries are bound to change their working strategies and rules. Industries will change how they operate and adopt newer and more efficient methods which depend on AI algorithms. The application of Artificial Intelligence to automate medical procedures and negate possible complications is not a new idea.

What is the Internet of Things(IoT)? So how does the Internet of Things disrupt digital marketing? Digital technology has blurred the line between online and offline life. From wearable technology that tracks health conditions around the clock, to watching favorite entertainment programs on the way to and from get off work, and remote access to electrical equipment at home. The shift to mobile devices has changed the way we interact with the world around us.

Many exciting things are coming up, hence no new documentation and test are going to be written until the official release of Leon. Feel free to join us on Discord to know more and to read the "A Much Better NLP and Future" blog post. Leon is an open-source personal assistant who can live on your server. He does stuff when you ask him to. You can talk to him and he can talk to you.

The recent advances of conversational recommendations provide a promising way to efficiently elicit users' preferences via conversational interactions. To achieve this, the recommender system conducts conversations with users, asking their preferences for different items or item categories. Most existing conversational recommender systems for cold-start users utilize a multi-armed bandit framework to learn users' preference in an online manner. However, they rely on a pre-defined conversation frequency for asking about item categories instead of individual items, which may incur excessive conversational interactions that hurt user experience. To enable more flexible questioning about key-terms, we formulate a new conversational bandit problem that allows the recommender system to choose either a key-term or an item to recommend at each round and explicitly models the rewards of these actions. This motivates us to handle a new exploration-exploitation (EE) trade-off between key-term asking and item recommendation, which requires us to accurately model the relationship between key-term and item rewards. We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. We theoretically prove that our algorithm can reduce the regret bound's dependency on the total number of items from previous work. We validate our proposed algorithms and regret bound on both synthetic and real-world data.

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.

Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labor, or data-driven optimization. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home. To get people's heads around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users. So, before jumping to those exciting and crazy artificial intelligence tools; first, we must know about What AI is.

Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.

Artificial intelligence (AI) has become a hot topic for countries worldwide, and both public- and private-sector organizations have already started leveraging it as a response to continuous digital disruption. According to IDC's 2022 Artificial Intelligence Spending Guide, global AI spending reached $88.6 billion in 2021, and it is forecast to grow at a compound annual growth rate (CAGR) of 25.6% over the 2021–2025 period. Canada, China, and the United States are among the countries in which many organizations began their AI journeys early, supported by government initiatives. Saudi Arabia is no different in terms of its commitment to becoming an AI powerhouse. As an extension of the country's Vision 2030, the Saudi Data and AI Authority (SDAIA) was established in 2019, followed by the release of the National Strategy for Data and AI in 2020.

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utilize the data from different online social networks while preserving user privacy remains largely unsolved. To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. We jointly consider information from partially aligned social networks with differential privacy guarantees. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. Next, to find user linkages across social networks, we make unsupervised user embedding-based alignment in which the user embeddings are achieved by the heterogeneous network embedding technology. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users. Extensive experiments on three real-world datasets demonstrate that our approach makes a significant improvement on user interest prediction tasks as well as defending user attribute inference attacks from embedding.