Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model. Traditionally, the generated recommendations are final, that is, the list of potential candidates is not modified unless the user explicitly changes his/her search criteria. In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step. This setting also allows for very uninformative initial search queries, since we pinpoint the user's intent due to the feedback during the search session. To achieve our goal, we employ an intent clustering method based on topic modeling which separates the candidate space into meaningful, possibly overlapping, subsets (which we call intent clusters) for each position. On top of the candidate segments, we apply a multi-armed bandit approach to choose which intent cluster is more appropriate for the current session. We also present an online learning scheme which updates the intent clusters within the session, due to user feedback, to achieve further personalization. Our offline experiments as well as the results from the online deployment of our solution demonstrate the benefits of our proposed methodology.

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn's job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings. We highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems: (1) The underlying query to the talent search system could be quite complex, combining several structured fields (such as canonical title(s), canonical skill(s), company name) and unstructured fields (such as free-text keywords). Depending on the application, the query could either consist of an explicitly entered query text and selected facets (talent search), or be implicit in the form of a job opening, or ideal candidate(s) for a job (talent recommendations).

Senior citizens often need support to live independently and actively participate in their communities. The EU-funded Alfred project came up with a Personal Interactive Assistant to help the elderly overcome the obstacles preventing them from carrying out everyday tasks. The project created a virtual'butler' to which people can talk, ask questions or give commands, and developed systems to encourage older people to socialise by suggesting and managing events, to monitor their state of health, and to help them stay physically and mentally active via personalised games. It produced 25 apps, both for immediate use and to inspire developers interested in designing new services that target the needs of senior citizens. The Alfred project brought together expertise and technology from a range of different areas: ubiquitous computing, big data, gaming, the semantic web, cyber physical systems, the internet of things, the internet of services, and human-computer interaction.

Last year, Bipin Preet Singh let go of his personal assistant. "A traditional secretary is just a messaging medium and not incredibly efficient," says the CEO and founder of MobiKwik. "Plus, there are some things that I wouldn't schedule through a secretary." For people who directly report to him, a secretary represented a layer of bureaucracy. We encourage interaction and informal conversations," Singh adds. Times have never been tougher for the secretary. Competing with virtual assistants, Google calender and mobile apps that can make bookings, take down minutes of meetings, store records and even send reminders to drink water, the secretary has lost some of his/her swag and salary. For an earlier generation of corporate leaders, having a secretary used to be a symbol of status, a measure of their professional success. But new-age managers scoff at the idea of having one. "Communication has evolved so much.

According to a 2017 University of Washington report, there are hundreds of millions of smart-home devices in more than 40 million U.S. homes. This number is expected to double by 2021. Amazon Echo, Google Home and other devices that have Alexa and Google Assistant built in, have proven to be some of the world's most promising new technologies. These AI-enabled assistants seem capable of doing everything, from turning on lights to answering simple and even complex questions. "OK Google" and "Alexa" have become common household phrases, as these smart connected speakers always have their microphones on, yet don't respond until their "wake words" are mentioned.

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called open-world learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach.

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays. Artificial Intelligence is transforming the way we live, and the tech giants are racing to stay ahead of the curve. AI-related funding totaled an estimated $15.2 billion in 2017, a 144% increase over the previous year. The U.S. tech industry leads with a 50% share of those investments, even with China swiftly closing the gap in terms of patents and AI research. AI itself isn't new, but boosted computing power, increased connectivity, and the sheer volume of data has paved the way for the fourth industrial revolution of AI. "The coming era will be looked back upon as the'AI era,' when AI became the defining competitive advantage for corporations, government agencies, and investment professionals," predicts David Nadler, founder of Kensho Technologies.

Artificial intelligence and machine learning are being adopted into the enterprise at a rapid clip and adoption is likely to surge in 2019. What comes next is the real business challenge: How will we manage technology that we likely don't understand? The issue is likely to bubble up in the year ahead. For now, most of us are lulled into thinking more algorithms are better and even assuming we can outsource critical thought to models. Why hurt our brains when we can trust Einstein, Watson, Alexa, Google Assistant, and other software tools to think for us?