Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., "It doesn't look good for a date"), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., "I prefer more romantic") in order to retrieve reviews pertaining to potentially better recommendations (e.g., "Perfect for a romantic dinner"). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.

Deb Norton spends her days helping teachers in Wisconsin's Oshkosh Area school district get more comfortable with technology tools they're using to engage students. A few years ago, she started seeing increasing mentions of artificial intelligence. Around then, the International Society for Technology in Education asked her to lead a course on the uses of artificial intelligence in the K-12 classroom. She was initially intrigued when she saw students light up at the mention of artificial intelligence. It soon became clear to her that they were already experiencing AI in their daily lives, with tools like Instagram filters or chatbots on websites.

Smartphones equipped with artificial intelligence apps take note of our usage data and behavior patterns to make suggestions and automate mundane tasks. Imagine you left your smartphone at home for a day. Can you even think of going through a day without your smartphone? An ever-increasing number of people would say "No." That's how dependent we've become on smartphone technology to deliver the latest in news, entertainment, education, communication, image enhancement, and more.

Ted Lasso is quite the eligible bachelor, but he isn't on Tinder, Bumble or Hinge. He and other characters on Apple TV's latest streaming sensation are on Bantr, a fictional dating app centered on playful conversation rather than traditional selfies and swipes. And while you can't buy a piece of Bantr, you can still bet on love the Lasso Way by exploring the growing list of fringe dating apps. The sector is unique in that consumers use several apps at a time. Since January 2019, App Annie data show U.S. users have spent a collective average of 7.4 million hours on dating apps a month on Android phones alone.

The US Federal Trade Commission (FTC) warns of extortion scammers targeting the LGBTQ community via online dating apps such as Grindr and Feeld. As the FTC revealed, the fraudsters would pose as potential romantic partners on LGBTQ dating apps, sending explicit photos and asking their targets to reciprocate. If they fall for the scammers' tricks, the victims will be blackmailed to pay a ransom, usually in gift cards, under the threat of leaking the shared sexual imagery with their family, friends, or employers. "To make their threats more credible, these scammers will tell you the names of exactly who they plan to contact if you don't pay up. This is information scammers can find online by using your phone number or your social media profile," the FTC said.

Data is the lifeblood of business – it drives innovation and enhances competitiveness. However, its importance was brought to the fore by the pandemic as lockdowns and social distancing drove digital transformation like never before. Forward-thinking businesses have started to grasp the importance of their data; they understand the consequences of not fully mobilizing it, but many are sat at the start of their journey. Even the best organizations are failing to extract the maximum benefits from their data while keeping it safe. This is where artificial intelligence (AI) comes into play – it can benefit enterprises with their data in three fundamental ways.

AI has become intertwined with every aspect of our lives. Each one of us is currently using this technology in one form or the other. From personal digital assistants like Siri, google assistant, Alexa, to self-driving cars, it's being used very widely. The use is increasing on a daily basis in fast growing sectors such as healthcare, finance, e-commerce, and manufacturing. Also, businesses like Swiggy and Zomato, which have invested heavily in AI over the past couple of years, have witnessed the power of technology to both sustain and increase growth.

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.