We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypothesize that data from one market can be used to improve performance in another. Only a few studies have been conducted in this area, partly due to the lack of publicly available experimental data. To this end, we collect and release XMarket, a large dataset covering 18 local markets on 16 different product categories, featuring 52.5 million user-item interactions. We introduce and formalize the problem of cross-market product recommendation, i.e., market adaptation. We explore different market-adaptation techniques inspired by state-of-the-art domain-adaptation and meta-learning approaches and propose a novel neural approach for market adaptation, named FOREC. Our model follows a three-step procedure -- pre-training, forking, and fine-tuning -- in order to fully utilize the data from an auxiliary market as well as the target market. We conduct extensive experiments studying the impact of market adaptation on different pairs of markets. Our proposed approach demonstrates robust effectiveness, consistently improving the performance on target markets compared to competitive baselines selected for our analysis. In particular, FOREC improves on average 24% and up to 50% in terms of nDCG@10, compared to the NMF baseline. Our analysis and experiments suggest specific future directions in this research area. We release our data and code for academic purposes.

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of 'asking clarifying questions in open-domain dialogues': (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.

Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.

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.