Dating app Tinder has launched a new tool called Explore that lets users search for potential matches based on their interests. In Explore, users can discover dates who share a love for'every mood and activity', such as gaming, music, food and – for those who want to form a'power couple' – entrepreneurship. Explore, found as a separate tab within the Tinder app, expands on previous filters that had helped users find a date – age, location and sexuality. By giving users the option to navigate through profiles arranged by interest, Tinder is giving users more control over who they meet, according to the company. Explore is now rolling out for users in the UK, the US, Australia and New Zealand, and will be available globally by mid-October.

Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, efficiently identifying the tasks that would benefit from co-training remains a challenging design question without a clear solution. In this paper, we suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss. On the large-scale Taskonomy computer vision dataset, we find this method can decrease test loss by 10.0\% compared to simply training all tasks together while operating 11.6 times faster than a state-of-the-art task grouping method.

With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intra- and inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create self-supervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision.

The goal of building intelligent dialogue systems has largely been \textit{separately} pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform goal-oriented functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two dialogue modes can potentially be intertwined together seamlessly in the same conversation, as easily done by a friendly human assistant. Such ability is desirable in conversational agents, as the integration makes them more accessible and useful. Our paper addresses this problem of fusing TODs and ODDs in multi-turn dialogues. Based on the popular TOD dataset MultiWOZ, we build a new dataset FusedChat, by rewriting the existing TOD turns and adding new ODD turns. This procedure constructs conversation sessions containing exchanges from both dialogue modes. It features inter-mode contextual dependency, i.e., the dialogue turns from the two modes depend on each other. Rich dependency patterns including co-reference and ellipsis are features. The new dataset, with 60k new human-written ODD turns and 5k re-written TOD turns, offers a benchmark to test a dialogue model's ability to perform inter-mode conversations. This is a more challenging task since the model has to determine the appropriate dialogue mode and generate the response based on the inter-mode context. But such models would better mimic human-level conversation capabilities. We evaluate baseline models on this task, including \textit{classification-based} two-stage models and \textit{two-in-one} fused models. We publicly release FusedChat and the baselines to propel future work on inter-mode dialogue systems https://github.com/tomyoung903/FusedChat.

This paper provides the first formalisation and empirical demonstration of a particular safety concern in reinforcement learning (RL)-based news and social media recommendation algorithms. This safety concern is what we call "user tampering" -- a phenomenon whereby an RL-based recommender system may manipulate a media user's opinions, preferences and beliefs via its recommendations as part of a policy to increase long-term user engagement. We provide a simulation study of a media recommendation problem constrained to the recommendation of political content, and demonstrate that a Q-learning algorithm consistently learns to exploit its opportunities to 'polarise' simulated 'users' with its early recommendations in order to have more consistent success with later recommendations catering to that polarisation. Finally, we argue that given our findings, designing an RL-based recommender system which cannot learn to exploit user tampering requires making the metric for the recommender's success independent of observable signals of user engagement, and thus that a media recommendation system built solely with RL is necessarily either unsafe, or almost certainly commercially unviable.

In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.

Artificial intelligence (AI) is responsible for driving autonomous vehicles, powering intelligent assistants such as Alexa and Siri, and placing annoying advertisements on web pages. AI has also improved many aspects of pediatric medicine, and played an important role in the COVID-19 pandemic. Voice recognition/dictation software is an example of AI that is currently used in pediatric practice. Today, Dragon Medical One from Nuance Communications, the most widely used voice recognition medical software, boasts a vocabulary of 300,000 words and integrates vocabularies for 90 medical specialties. By integrating deep learning (DL), the software covers the nuances of the user's speech patterns and improves over time, achieving 99% accuracy.1

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The Command Prompt app has been around since December 1987, providing Windows users with a command-line interface from which to execute operating systems tasks, many of which are very useful. Due to its popularity, Microsoft has made the app easily accessible on Windows 10, in more ways than one. Here are 11 ways to open Command Prompt. You can search for any app using the Windows search bar, and the Command Prompt is no exception. You can also open Command Prompt from the Start menu.

The app has added features to find matches in other ways, profiles that allow users to record snippets on their interests to Tinder Passport, a paid product that gives users the chance to find matches across the world. The company has also expanded into interactive dating features, such as Vibes, a 48-hour event in the app that asks users to respond to a series of questions to match with others who participate. The new Explore section includes more than 15 types of interests, such as "foodies," "gamers," and "animal parents." Different interests will surface to users depending on their locations, the time of day and their own passions. It is available to Tinder users in the U.S., U.K, Australia and New Zealand to start.