We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.

A large amount of data accommodated in knowledge graphs (KG) is actually metric. For example, the Wikidata KG contains a plenitude of metric facts about geographic entities like cities, chemical compounds or celestial objects. In this paper, we propose a novel approach that transfers orometric (topographic) measures to bounded metric spaces. While these methods were originally designed to identify relevant mountain peaks on the surface of the earth, we demonstrate a notion to use them for metric data sets in general. Notably, metric sets of items inclosed in knowledge graphs. Based on this we present a method for identifying outstanding items using the transferred valuations functions 'isolation' and 'prominence'. Building up on this we imagine an item recommendation process. To demonstrate the relevance of the novel valuations for such processes we use item sets from the Wikidata knowledge graph. We then evaluate the usefulness of 'isolation' and 'prominence' empirically in a supervised machine learning setting. In particular, we find structurally relevant items in the geographic population distributions of Germany and France.

In the last decade, we have seen a qualitative jump in the performance of machine learning (ML) methods directed at narrow, well-defined tasks. For example, there has been marked progress in object recognition [57], game-playing [73], and generative models of images [40] and text [39]. Some of these methods have achieved superhuman performance within their domain [73, 64]. In each of these cases, a powerful ML model was trained using large amounts of data on a highly complex task to surpass what was commonly believed possible. Here we consider the transpose of this situation.

To the delight of binge-watchers everywhere, it's no longer prohibitively expensive to purchase a giant television. And those devices are also getting smarter, with features like voice commands, personalized recommendations, and built-in apps for Netflix and other streaming services. It's almost impossible to buy a TV without them. The average consumer might ascribe the declining price to a variant of Moore's law. "Right now, you're paying with your data, but you don't know the price," says Casey Oppenheim, CEO of Disconnect, a privacy-focused software company.

Facebook today announced the open source release of Deep Learning Recommendation Model (DLRM), a state-of-the-art AI model for serving up personalized results in production environments. DLRM can be found on GitHub, and implementations of the model are available for Facebook's PyTorch, Facebook's distributed learning framework Caffe2, and Glow C . Recommendation engines decide a lot of what people see today, whether it's content on social media sites like Facebook, ecommerce sites like Amazon, or even the first options you see on an Xbox. Last month, Amazon made its AI for the shopping recommendations system Personalize available on AWS. A paper by more than 20 Facebook AI researchers published on arXiv in late May explains how the model uses embedding tables that map categorical data to representations.

AI has got an unbelievable momentum in the past couple of years. The current intelligent frameworks have the capability of managing a lot of data and simplifying complicated calculations very fast. In any case, these are not the sentient machines. AI developers are trying to build up this feature in the future. And in the coming years, the AI framework will reach and surpass the performance of humans in solving different tasks.

Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not been much research in developing recommendation systems to help borrowers make wise decisions. On P2PL platforms, borrowers can either apply for bidding loans, where the interest rate is determined by lenders bidding on a loan or traditional loans where the P2PL platform determines the interest rate. Different borrower grades -- determining the credit worthiness of borrowers get different interest rates via these two mechanisms. Hence, it is essential to determine which type of loans borrowers should apply for. In this paper, we build a recommendation system that recommends to any new borrower the type of loan they should apply for. Using our recommendation system, any borrower can achieve lowered interest rates with a higher likelihood of getting funded.

Artificial intelligence is one of the most significant breakthroughs of the 21st century. Experts from different industries study its capabilities and discover new ways of its application. We call AI an emerging technology, however, scientists have been working in this direction since the 1950s. At first, AI was far from smart robots we see in sci-fi movies. Nevertheless, thanks to such technologies as machine learning and deep learning, AI became one of the most promising areas of the IT industry.

Make modifications easily on multiple platforms at one go Reach customers on channels of their choice e.g. WhatsApp, Facebook Messenger, Skype Easily transact on your bot e.g. Easily transact on your bot e.g.

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions.