A Recommender System predicts the likelihood that a user would prefer an item. Based on previous user interaction with the data source that the system takes the information from (besides the data from other users, or historical trends), the system is capable of recommending an item to a user. Think about the fact that Amazon recommends you books that they think you could like; Amazon might be making effective use of a Recommender System behind the curtains. This simple definition, allows us to think in a diverse set of applications where Recommender Systems might be useful. Applications such as documents, movies, music, romantic partners, or who to follow on Twitter, are pervasive and widely known in the world of Information Retrieval.

Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

Generation and evaluation of crowdsourced content is commonly treated as two separate processes, performed at different times and by two distinct groups of people: content creators and content assessors. As a result, most crowdsourcing tasks follow this template: one group of workers generates content and another group of workers evaluates it. In an educational setting, for example, content creators are traditionally students that submit open-response answers to assignments (e.g., a short answer, a circuit diagram, or a formula) and content assessors are instructors that grade these submissions. Despite the considerable success of peer-grading in massive open online courses (MOOCs), the process of test-taking and grading are still treated as two distinct tasks which typically occur at different times, and require an additional overhead of grader training and incentivization. Inspired by this problem in the context of education, we propose a general crowdsourcing framework that fuses open-response test-taking (content generation) and assessment into a single, streamlined process that appears to students in the form of an explicit test, but where everyone also acts as an implicit grader. The advantages offered by our framework include: a common incentive mechanism for both the creation and evaluation of content, and a probabilistic model that jointly models the processes of contribution and evaluation, facilitating efficient estimation of the quality of the contributions and the competency of the contributors. We demonstrate the effectiveness and limits of our framework via simulations and a real-world user study.

Prerequisite relations among concepts play an important role in many educational applications such as intelligent tutoring system and curriculum planning. With the increasing amount of educational data available, automatic discovery of concept prerequisite relations has become both an emerging research opportunity and an open challenge. Here, we investigate how to recover concept prerequisite relations from course dependencies and propose an optimization based framework to address the problem. We create the first real dataset for empirically studying this problem, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels. Experiment results on a synthetic dataset and the real course dataset both show that our method outperforms existing baselines.

Too many laptops, tablets and other mobile devices leads to congested WiFi airwaves. Q: I have horrible Wi-Fi in rooms of my house. A: A wireless network repeater is a great way to extend coverage, but it has to be placed in the right spot. Most routers spread signals in a circle. The closer you are to the router, the stronger the signal.

The OOH can help you find career information on duties, education and training, pay, and outlook for hundreds of occupations. Funeral service workers organize and manage the details of a funeral. Insurance underwriters decide whether to provide insurance and under what terms. They evaluate insurance applications and determine coverage amounts and premiums. Computer and information research scientists invent and design new approaches to computing technology and find innovative uses for existing technology. They study and solve complex problems in computing for business, medicine, science, and other fields. Mathematicians conduct research to develop and understand mathematical principles. They also analyze data and apply mathematical techniques to help solve real-world problems. Atmospheric scientists study the weather and climate, and how those conditions affect human activity and the earth in general. Economists study the production and distribution of resources, goods, and services ...

Embedding making, political analysis, and intelligence analysis; morality when handling preferences and dealing models of biomedical argumentation in research journals with the potential and risks of big data were identified and popular media; annotation of rhetorical figures; as challenging endeavors for the future.

We consider a crowdsourcing model in which n workers are asked to rate the quality of n items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manually evaluate the quality of a small number of items, and wishes to curate together almost all of the high-quality items with at most an fraction of low-quality items. Perhaps surprisingly, we show that this is possible with an amount of work required of the manager, and each worker, that does not scale with n: the dataset can be curated with $\tilde{O}(1/\beta\alpha\epsilon^4)$ ratings per worker, and $\tilde{O}(1/\beta\epsilon^2)$ ratings by the manager, where $\beta$ is the fraction of high-quality items. Our results extend to the more general setting of peer prediction, including peer grading in online classrooms.

We introduce a class of network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of statistical network models, this class is distinguished by permitting the location of a new edge to explicitly depend on the structure of the graph, but being nonetheless statistically and computationally tractable. In the limit of infinite walk length, the model converges to an extension of the preferential attachment model---in this sense, it can be motivated alternatively by asking what preferential attachment is an approximation to. Theoretical properties, including the limiting degree sequence, are studied analytically. If the entire history of the graph is observed, parameters can be estimated by maximum likelihood. If only the final graph is available, its history can be imputed using MCMC. We develop a class of sequential Monte Carlo algorithms that are more generally applicable to sequential random graph models, and may be of interest in their own right. The model parameters can be recovered from a single graph generated by the model. Applications to data clarify the role of the random walk length as a length scale of interactions within the graph.

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time.