Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query, item) combinations, often referred to as the cold start problem. Furthermore, the search system can be biased towards items that are frequently shown to a query previously, also known as the 'rich get richer' (a.k.a. feedback loop) problem. In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation. In this paper, we propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. First, it learns representations of items and their natural-language features by predicting (item, item) correlation graphs derived from the recommender system as an auxiliary task. Then, the learned representations are transferred to solve the target search retrieval task, performing query-to-item prediction without having seen any (query, item) pairs in training. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned. We demonstrate that the proposed approach can achieve high performance on offline search retrieval tasks, and more importantly, achieved significant improvements on relevance and user interactions over the highly-optimized production system in online experiments.

Voice technology has come a long way. With smart assistants like Alexa, Google Assistant, Cortana, and so forth, daily chores and tedious tasks are now a cakewalk. You come home from work and the light turns on and the coffee machine starts to brew. You don't have to worry about the washing machine staying on or whether you forgot to turn the kitchen lights off when you've already hit the bed. Smart voice assistants are now your personal butlers, thanks to voice technology.

Nuisance phone calls could disappear with the help of a virtual assistant that screens out spammers before the phone even rings. Robocalls, in which an automated recording pretends to be a human, are a common problem – there are an estimated 4.9 million every hour in the US alone. Simple block lists can screen some of these calls out, but they are only around 60 per cent effective, says Sharbani Pandit at the Georgia Institute of Technology.

Get your team access to 4,000 top Udemy courses anytime, anywhere. Welcome to my course on building your first Google Assistant Application using Java and Spring Boot framework. I am happy to present you the step by step process of building the application that will be integrated with your own Google Assistant device. But first, what is a Google Assistant? Why you should create a Google Assistant application?

Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. By extending the objective function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we further specialize it for top-k recommendation with implicit feedback. Our extensive experiments on several real-world datasets show that JoVA-Hinge outperforms a broad set of state-of-the-art collaborative filtering methods, under a variety of commonly-used metrics. Our empirical results also confirm the outperformance of JoVA-Hinge over existing methods for cold-start users with a limited number of training data.

Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. Decreased users' willingness to share personal information along with data minimization/protection policies (such as the European GDPR), can result in the "data scarcity" dilemma affecting data-intensive applications such as recommender systems (RS). We argue that scarcity of adequate data due to privacy concerns can severely impair the quality of learned models and, in the long term, result in a turnover and disloyal customers with direct consequences for lives, society, and businesses. To address these issues, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning to rank optimization by following the Federated Learning principles conceived originally to mitigate the privacy risks of traditional machine learning. We have conducted an extensive experimental evaluation on three Foursquare datasets and have verified the effectiveness of the proposed architecture concerning accuracy and beyond-accuracy objectives. We have analyzed the impact of communication cost with the central server on the system's performance, by varying the amount of local computation and training parallelism. Finally, we have carefully examined the impact of disclosed users' information on the quality of the final model and ...

Accelerate administrative processes -- administrative processes are often mundane tasks such as coordinating meeting requests, booking travel or making notes during meetings. A virtual assistant can do many of these tasks. The more advanced AI will become, the more tasks these virtual assistants can perform. Drive innovation -- AI can help advance your R&D activities by providing insights into your customers' (latent) needs, combined with how they use your products. That information can help to speed-up innovation Augment your employees to make them more effective and efficient -- AI can provide your employees with the right information at the right moment to make them more efficient and effective, but also exoskeletons can augment your employees to make repetitive hard work easier. Ford has been rolling out exoskeletal technology globally to help employees who perform repetitive overhead tasks. Such AI skeletons can help employees accomplish accuracy and precision even when faced with immense complexity. For example, JP Morgan Chase has implemented chatbots in their IT department to handle 1.7 million requests per year, doing the work of 140 people.

Are you a digital native? The answer is a definite "Yes" as we call out Siri and Alexa to respond to our instructions. Artificial Intelligence is the ability for computers to do things that are considered attributes of intelligence: processing language, understanding pictures, detecting patterns, etc. Interestingly, many movies and TV shows have featured predictions as to what A.I. might look like in the future. One such place is the Blockbuster franchise, Star Wars. In addition, in the Marvel Universe, most of the depictions of A.I. originate as digital assistant to either hero or villain.

Construction of potential machines that excels the task of interpreting, analysing and applying data processed by human mind for the betterment of technology and the world as a whole. AI has its presence in almost everything that whirls around us. It would be precise to state "AI has changed the way we look at everything around us. The famous Siri, Cortana, Alexa, Google Assistant are well known voice assistant powered by AI. Healthcare, Education, Media, Transportation, Manufacturing are the notable sectors where AI is of major use.

The aim of a recommender system is to recommend one or more items to users of the system, e.g. A user might be a person, a group of persons, or another entity with item preferences. Content-base Recommender: makes use of features for both users and items. Users can be described by properties such as age or gender. Items can be described by properties such as the author or the manufacturer. Typical examples of content-based recommendation systems can be found on social matchmaking sites.