Gartner predicts that "by 2022, 70 percent of white-collar workers will interact with conversational platforms on a daily basis." As a result, the research group found that more organizations are investing in chatbot development and deployment. IBM Business Partners like Sopra Steria are making chatbot and virtual assistant technology available to businesses. Sopra Steria, a European leader in digital transformation, has developed an intelligent virtual assistant for organizations across several industries who want to use an AI conversational interface to answer recurrent customer service questions. In developing our solution, we at Sopra Steria were looking for AI technology that was easy to configure and could support multiple languages and complex dialogs.

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.

You don't consider yourself the forgetful type, yet somehow you can't seem to find your smartphone on a daily basis. It's not unusual to misplace your car keys or reading glasses (which are on your head). And wasn't your wallet on the kitchen counter a moment ago? OK, so maybe you've had a lot on your mind, these days. The good news, however, is technology can help you find your stuff.

Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks. In continuous control tasks, widely used policies with Gaussian distributions results in ineffective exploration of environments and limited performance of algorithms in many cases. In this paper, we propose a density-free off-policy algorithm, Generative Actor-Critic(GAC), using the push-forward model to increase the expressiveness of policies, which also includes an entropy-like technique, MMD-entropy regularizer, to balance the exploration and exploitation. Additionnally, we devise an adaptive mechanism to automatically scale this regularizer, which further improves the stability and robustness of GAC. The experiment results show that push-forward policies possess desirable features, such as multi-modality, which can improve the efficiency of exploration and asymptotic performance of algorithms obviously.

Recommendation systems have been extensively studied by many literature in the past and are ubiquitous in online advertisement, shopping industry/e-commerce, query suggestions in search engines, and friend recommendation in social networks. Moreover, restaurant/music/product/movie/news/app recommendations are only a few of the applications of a recommender system. A small percent improvement on the CTR prediction accuracy has been mentioned to add millions of dollars of revenue to the advertisement industry. Click-Through-Rate (CTR) prediction is a special version of recommender system in which the goal is predicting whether or not a user is going to click on a recommended item. A content-based recommendation approach takes into account the past history of the user's behavior, i.e. the recommended products and the users reaction to them. So, a personalized model that recommends the right item to the right user at the right time is the key to building such a model. On the other hand, the so-called collaborative filtering approach incorporates the click history of the users who are very similar to a particular user, thereby helping the recommender to come up with a more confident prediction for that particular user by leveraging the wider knowledge of users who share their taste in a connected network of users. In this project, we are interested in building a CTR predictor using Graph Neural Networks complemented by an online learning algorithm that models such dynamic interactions. By framing the problem as a binary classification task, we have evaluated this system both on the offline models (GNN, Deep Factorization Machines) with test-AUC of 0.7417 and on the online learning model with test-AUC of 0.7585 using a sub-sampled version of Criteo public dataset consisting of 10,000 data points.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Chrissy Teigen is giving her two cents on the recent claims that A-list stars Ben Affleck and Matthew Perry are pursuing young women on dating apps. Teigen, who is married to musician John Legend, appears to have caught wind of the celeb dating app stories making their way around the Internet this week. First, Affleck went viral after a woman named Nivine Jay shared a video she received of the actor after matching with him on Raya.

An institution, be it a body of government, commercial enterprise, or a service, cannot interact directly with a person. Instead, a model is created to represent us. We argue the existence of a new high-fidelity type of person model which we call a digital voodoo doll. We conceptualize it and compare its features with existing models of persons. Digital voodoo dolls are distinguished by existing completely beyond the influence and control of the person they represent. We discuss the ethical issues that such a lack of accountability creates and argue how these concerns can be mitigated.

We hear that machines are capable of many things we humans are. We know that they are even capable of more than us in some areas. But what about the fields that are harder for them or even impossible? Some AIs create music, write articles and even paint for us now. All of these are pieces that require some kind of emotional attachment to them, and many of them are considered art when created by humans.

If you're a parent or often find yourself needing to wrangle a group of people in your household, Google's latest Assistant update might be helpful. It's expanding the Broadcast tool that was previously limited to its smart speakers and displays to iPhones and Android devices. The company also announced a set of new features for the Assistant, including stories, games, songs and a Mother's Day surprise. Broadcast lets you send a message to all compatible devices at once, and you can create groups to specify who you want to reach. Google allows you to set up a Family group for up to six of your relatives, and the expansion being announced today will let you reach these members on their phones too. Just as they already could from a Nest speaker or display, your contact can now reply to your message from their Android or iPhone.

Looking to summon the entire family even when some loved ones are out and about? Google Assistant's Broadcast feature can now do just that, thanks to a recent update. Google is also rolling out a long-awaited improvement to the Assistant's Family Bell feature. Set to go live today, Google Assistant's enhanced Broadcast feature can now reach members of your family group on their phones as well as on Google smart speakers and displays. For example, you could say "Hey Google, tell my family, dinner will be ready in an hour" from the Google Nest Hub Max in the kitchen, and Google Assistant will broadcast the message to all the other Google speakers and displays in your home, as well as on the iPhones and Android phones of any on-the-go family members.