Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. Traditional models usually motivate themselves by designing complex or tailored architectures based on different assumptions. However, the training data of recommender system can be extremely sparse and imbalanced, which poses great challenges for boosting the recommendation performance. To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem. The core of our model lies in the counterfactual question: "what would be the user's decision if the recommended items had been different?". To answer this question, we firstly formulate the recommendation process with a series of structural equation models (SEMs), whose parameters are optimized based on the observed data. Then, we actively indicate many recommendation lists (called intervention in the causal inference terminology) which are not recorded in the dataset, and simulate user feedback according to the learned SEMs for generating new training samples. Instead of randomly intervening on the recommendation list, we design a learning-based method to discover more informative training samples. Considering that the learned SEMs can be not perfect, we, at last, theoretically analyze the relation between the number of generated samples and the model prediction error, based on which a heuristic method is designed to control the negative effect brought by the prediction error. Extensive experiments are conducted based on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data.In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.

A technology based on artificial intelligence is helping to spot biomarkers and document the progression of amyotrophic lateral sclerosis (ALS) in a large speech study being conducted by EverythingALS. The technology, developed by Modality.ai, is a web-based computer program that uses audio (speech) and video (facial) recordings to assess neurological states automatically through AI and machine learning algorithms. Its greatest advantage is that data can be collected remotely at home on any computer device with the help of a virtual assistant called "Tina." This is important for people with ALS, who often have limited mobility due to muscle weakness, which may affect their ability to participate in clinical studies. "Our mission is to discover and deploy initiatives that focus on new ways to diagnose and treat neurological disorders at the intersection of computing and brain science with a focus on ALS," Indu Navar, CEO and co-founder of EverythingALS, a U.S. nonprofit that is part of the Peter Cohen Foundation, said in a press release.

Labor Day has come and gone, leaving us in that weird period right before fall. It's not quite summer and it's not quite autumn; it's hot and cold; there are green leaves and a few red ones. We've brought in a fresh crop of discounts just in time for all the upcoming harvest festivals. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like).

Machine learning is one of the technology that has become more and more popular with time and machine learning is the subset of the Artificial Intelligence which comes to your knowledge when you are connected to IT industry. Most of the companies like Netflix, Google and smaller companies uses Machine learning algoithms to predict the insights from the data. Although terms like artificial intelligence, machine learning and deep learning are used interchangeably but, they are not the same thing. Machine learning is the subset of artificial intelligence and deep learning is a subset of machine learning. Alan Turing's vision towards machine learning is being explained in one of his seminal paper such as " Machine learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and make future predictions. The performance of such a system should be at least human level."

A.I. is this year's buzzword of choice across the Tech industry, and speculation about what this field can achieve is already running rife. Let's separate fact from fiction and make some sense of all the hype. As we start the new year, the Tech propaganda machine is already ramping up its next generation of buzzwords, promising paradigm shifts and silver bullets that will make whole industries obsolete, enable huge efficiency gains, and make the world a better place. Blockchain, which used to top keyword search trends and social media posts, suffered a significant decline in interest, partly due to the fact that its initial hype was residual from the Bitcoin bubble. It seems that this year's buzzword of choice is going to be Artificial Intelligence.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.

We study the matrix completion problem that leverages hierarchical similarity graphs as side information in the context of recommender systems. Under a hierarchical stochastic block model that well respects practically-relevant social graphs and a low-rank rating matrix model, we characterize the exact information-theoretic limit on the number of observed matrix entries (i.e., optimal sample complexity) by proving sharp upper and lower bounds on the sample complexity. In the achievability proof, we demonstrate that probability of error of the maximum likelihood estimator vanishes for sufficiently large number of users and items, if all sufficient conditions are satisfied. On the other hand, the converse (impossibility) proof is based on the genie-aided maximum likelihood estimator. Under each necessary condition, we present examples of a genie-aided estimator to prove that the probability of error does not vanish for sufficiently large number of users and items. One important consequence of this result is that exploiting the hierarchical structure of social graphs yields a substantial gain in sample complexity relative to the one that simply identifies different groups without resorting to the relational structure across them. More specifically, we analyze the optimal sample complexity and identify different regimes whose characteristics rely on quality metrics of side information of the hierarchical similarity graph. Finally, we present simulation results to corroborate our theoretical findings and show that the characterized information-theoretic limit can be asymptotically achieved. N recent years, personalized recommender systems have emerged in an extensive range of Web applications to predict the preferences of its users and provide them with new and relevant items based on the scarce data about the users and/or items [2]. There are two major paradigms of recommender systems: (i) content-based filtering systems; (ii) collaborative filtering systems. Content-based filtering approach exploits a profile of users' preferences and/or properties of the items to carry out the recommendation task.

We have heard for years that remarkable innovations in the field of artificial intelligence (AI) will change virtually every aspect of our lives. Artificial intelligence has been used for years in areas we are not even aware of. Siri analyzes what we say. A robot dog that decides how to behave. Tesla, which, even on winding, mountainous, and most importantly Polish roads, can travel several kilometers without a minor stutter and without a problem, fully autonomously. Have you wondered in which direction this technology will develop?

Artificial intelligence (AI) is machines that can simulate or replicate certain human cognitive functions. More specifically, AI software can affect the thinking behind decisions, create models that make decisions more efficiently, process information more quickly, and respond more rapidly to changes in the environment. For example, an AI program called Lounges can predict whether someone at the office will buy something from them based on previous purchases or whether they'll meet with a client at a specific time and place based on their social media posts. Artificial intelligence is made up of software and systems that can perform tasks that humans performed in places like Apple computers and Siri. AI has become very popular in popular culture in the last few years, being used in numerous movies and television shows.