Data is the new game-changer, everywhere. According to reports, data-driven organizations are 19 times more likely to be profitable. Data and analytics are critical components of digital transformation. Considering the rate at which data is being generated, its analysis is becoming a hefty task. Organizing large volumes of real-time data from several sources is time-consuming and tedious. To reduce the human effort involved in this and decrease the required time, AI and ML are being employed.

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.

Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.

The speed of technological change is exponential. What was yesterday's hot ticket quickly becomes tomorrow's old news. We are living in the midst of a huge surge of interest and research in Artificial Intelligence (AI). It seems like every week there is a new breakthrough in the field and a new record is set in some task previously done by humans. If you don't already know what IoT, AI, VR, AR, and bots mean, you better get up to speed immediately because these technologies are changing the way data is created, collected, interpreted, and communicated.

'Labor of Love,' hosted by'Sex and the City' star Kristin Davis, follows a woman as she searches for love and motherhood. Love is blind -- and bald too, thanks to an app catering to singles with hair loss. Bald Dating, a new matchmaking site that launched Monday, is aiming to make it easier for singles with hair loss to find "the one" without focusing on hair follicles. The website is being promoted using the tagline "heads & personalities shine." Now there's a niche dating app aiming to connect bald singles with partners who have an affinity for it.

Smart speakers have become so ubiquitous lately that you most likely have more than one set up at home. Whether you're using smart speakers from Google, Amazon, or Apple, you can send audio to several speakers at once, configure them as stereo pairs, or even get your music to follow you from room to room. Google not long ago rebranded its Home speakers as Nest speakers, so you might have one or more of each--but they'll still work together no matter what the label says. Speaker management is handled through the Google Home app for Android or iOS. Open up the app and you'll see all your Nest speakers listed, together with any other connected smart home devices like Chromecasts and Nest cameras.

You've thought long and hard about it, and you're finally ready to buy a smart speaker. They're all being heavily marketed for Black Friday sales, and you'd like to ask Siri, Alexa or the Google Assistant to play music, tell you the weather, turn off your TV or lights on command. But you're stuck: Which brand and model to buy? Amazon has five Echo speakers currently available, Google has six and Apple has two. Which one is right for you? And then there are the concerns about security and snooping.

On May 8, 2018, Google I/O was held at Shoreline Amphitheatre in Mountain View, California. If you are wondering what Google I/O is, don't worry, I've got your back. In the Keynote, Sundar Pichai, the CEO of Alphabet Inc. (Google's parent company), shared the then-latest developments that Google had been working on. One of the projects that he spoke about was something that maybe no one saw coming; an application of Artificial Intelligence (AI), soon to be on our own smartphones, that left the world in awe. The project was called'Google Duplex'. This initiative enables AI to place a phone call to a hair salon, converse just like us humans, and book a haircut appointment - and the part where your jaws drop is that all of this takes place in the background on your phone, without any intervention of yours!

Digital marketing in the modern era is first and foremost about data. With the huge amount of data available, it is increasingly common to see marketing become the top priority for many businesses because it is directly linked to increasing revenue. Businesses these days need to understand consumer behavior to optimize marketing campaigns. In this article, we'll look at how machine learning can help businesses improve and strengthen their marketing efforts. Also called statistical learning, machine learning is part of the race for useful information, which leads to rationalized decision-making.

A wind of innovation is blowing in the artificial intelligence sector. As artificial intelligence develops, its use cases diversify. Many companies are emerging and exploiting this technology in a relevant and innovative way. Artificial intelligence and machine learning are increasingly popular among companies in all industries. However, AI algorithms tend to overwork processors and GPUs.