Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem. The problem is commonly-existed and challenging due to two main aspects, i.e, there are only positive user-item ratings on the target domain and there is no overlapped user across different domains. Most previous CDR approaches cannot solve the RNCDR problem well, since (1) they cannot effectively combine review with other information (e.g., ID or ratings) to obtain expressive user or item embedding, (2) they cannot reduce the domain discrepancy on users and items. To fill this gap, we propose Collaborative Filtering with Attribution Alignment model (CFAA), a cross-domain recommendation framework for the RNCDR problem. CFAA includes two main modules, i.e., rating prediction module and embedding attribution alignment module. The former aims to jointly mine review, one-hot ID, and multi-hot historical ratings to generate expressive user and item embeddings. The later includes vertical attribution alignment and horizontal attribution alignment, tending to reduce the discrepancy based on multiple perspectives. Our empirical study on Douban and Amazon datasets demonstrates that CFAA significantly outperforms the state-of-the-art models under the RNCDR setting.

The glitches we often see in conversational interfaces like Alexa and Siri reveal to us the unique human ability we have to deploy empathy in conversations and social life. But in order for artificially intelligent machines to learn, more and more we must express ourselves in a reduced language and must simplify the complex range of human expression into something AI can understand. As we learn to live within the narrow confines of the computer,we may increasingly begin to lose the creative expressive potential of our bodies and language,writes David Berry. The imitation game, better known as the Turing test, was developed by Alan Turing in 1950. As one of the early pioneers of computers, he argued that if a computer could imitate a human successfully, it might thereby be thought of as intelligent.

Grindr is tightening privacy controls for the Olympic Village in Beijing. Bloomberg has learned the gay dating app is blocking people outside the Village from using the location-based Explore feature to find athletes in or near the area. The move is meant to protect athletes from harassment or persecution so they can "feel confident" connecting with each other during the Winter Olympic Games, Grindr for Equality director Jack Harrison-Quintana said. Anyone who uses Grindr inside the Village will see a pop-up telling them people outside the area can't browse the locale using Explore. "Your privacy is important to us," Grindr says in the alert.

One of the stars of the working-out-from-home boom is struggling. Peloton won't go quietly though and is making some big changes. The company will replace the CEO and co-founder, John Foley, who will become executive chairman, with former Spotify COO Barry McCarthy reportedly set to step into his shoes. While Foley is sticking around, the company is cutting around 2,800 corporate positions -- these won't include Peloton's instructors who lead its live classes. The company said in a press release about the lay-offs that its "monthly membership will be complimentary for impacted team members for an additional 12 months."

In a busy hospital, a radiologist uses an artificial intelligence system to help her diagnose medical conditions based on patients' X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI's predictions? Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction -- which may look convincing but still be wrong -- to make an estimation. To help people better understand when to trust an AI "teammate," Massachusetts Institute of Technology researchers created a technique that guides humans to a more accurate understanding of when a machine makes correct predictions and when it makes incorrect ones. The research is supported by the U.S. National Science Foundation.

Consumers have incredibly high expectations for companies to deliver faster and more personalized experiences, which is fueling the demand for Artificial Intelligence (AI) solutions. In turn, the increase in this utilization of AI is driving growth for many startups. Here are some AI tactics you can use today to scale and grow your business. Around 90 percent of consumers consider an immediate response important, and they demand a connected experience whenever they interact with a brand. Therefore, more organizations are investing in smarter solutions for better customer support. One of the simplest ways to do this is to embrace AI-powered applications, such as chatbots, voice search, and virtual assistants to improve customer experience.

Below are some AI applications that you may not realise are AI-powered: Online shopping and advertising Artificial intelligence is widely used to provide personalised recommendations to people, based for example on their previous searches and purchases or other online behaviour. AI is hugely important in commerce: optimising products, planning inventory, logistics etc. Web search Search engines learn from the vast input of data, provided by their users to provide relevant search results. Digital personal assistants Smartphones use AI to provide services that are as relevant and personalised as possible. Virtual assistants answering questions, providing recommendations and helping organise daily routines have become ubiquitous. Machine translations Language translation software, either based on written or spoken text, relies on artificial intelligence to provide and improve translations.

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. "Google I/O brings together developers from around the globe annually for talks, hands-on learning with Google experts, and the first look at Google's latest developer products." 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 creation of novel recommendation algorithms for social networks is currently struggling with the volume of available data originating in such environments. Given that social networks can be modeled as graphs, a distributed graph-oriented support to exploit the computing capabilities of clusters arises as a necessity. In this thesis, a platform for graph storage and processing named Graphly is proposed along with GraphRec, an API for easy specification of recommendation algorithms. Graphly and GraphRec hide distributed programming concerns from the user while still allowing fine-tuning of the remote execution.

Recommender systems have become essential tools in many application areas as they help alleviate information overload by tailoring their recommendations to users' personal preferences. Users' interests in items, however, may change over time depending on their current situation. Without considering the current circumstances of a user, recommendations may match the general preferences of the user, but they may have small utility for the user in his/her current situation.We focus on designing systems that interact with the user over a number of iterations and at each step receive feedback from the user in the form of a reward or utility value for the recommended items. The goal of the system is to maximize the sum of obtained utilities over each interaction session. We use a multi-armed bandit strategy to model this online learning problem and we propose techniques for detecting changes in user preferences. The recommendations are then generated based on the most recent preferences of a user. Our evaluation results indicate that our method can improve the existing bandit algorithms by considering the sudden variations in the user's feedback behavior.