Match is becoming the first major dating app to provide its premium users with personally-tailored advice through a free human coach. The company announced today that it is beginning to roll out a new service called AskMatch which allows its paid users to chat on the phone with one of the company's dating hired'experts.' According to a report from TechCrunch, Match members can pick their coach's brains on a variety of topics that include how to set up a good dating profile, getting over a break up, or more general advice on dating. In multiple phone interviews, Match CEO, Hesam Hosseini said that the service will help to push the online dating platform, which has been in existence since 1995, into the future. 'Match's mission has always been around relationships and bringing people together.

In a gallery in downtown Manhattan, people are huddling around four laptops, taking turns to control the apartments of 14 complete strangers. They watch via live video feeds, and respond whenever the residents ask "Someone" to help them. They switch the lights on and off, boil the kettle, put some music on – whatever they can do to oblige. The project, called Someone, is the latest in a series exploring our ever more complicated relationship with technology. It's by the American artist Lauren McCarthy and is a sort of outsourcing of Lauren, an earlier work in which she acted as a real-life Alexa, remotely watching over a home 24 hours a day, responding to its occupants' questions and needs like a flesh and blood version of Amazon's voice-operated virtual assistant. Lauren, a video work, features in AI: More Than Human, which opens this week at the Barbican in London as part of its Life Rewired season, an investigation into what it means to be human in the digital era.

In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preference. User preferences on items tend to change over time due to a variety of factors such as change in context, the task being performed, or other short-term or long-term external factors. Recommender systems, in particular need to be able to capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework which takes into account the dynamics of user preferences. We propose an approach based on Hidden Markov Models (HMM) to identify change-points in the sequence of user interactions which reflect significant changes in preference according to the sequential behavior of all the users in the data. The proposed framework leverages the identified change points to generate recommendations using a sequence-aware non-negative matrix factorization model. We empirically demonstrate the effectiveness of the HMM-based change detection method as compared to standard baseline methods. Additionally, we evaluate the performance of the proposed recommendation method and show that it compares favorably to state-of-the-art sequence-aware recommendation models.

For example, while a recommendation of their behavior such as browsing trails, bookmarks ratings, source based on co-authorship links ranks attendees or created social links. It enables modern recommender systems by its social similarity with the target user, the recommendation to use multiple sources of information about user interests case might require to find attendees who are interested and preferences to deliver better recommendations. This in similar topics while being most likely unknown is most frequently done using parallel hybrid recommendation to the target user (i.e., having the weakest social similarity).

The accuracy of Top-N recommendation task is challenged in the systems with mainly implicit user feedback considered. Adversarial training has presented successful results in identifying real data distributions in various domains (e.g. image processing). Nonetheless, adversarial training applied to recommendation is still challenged especially by interpretation of negative implicit feedback causing it to converge slowly as well as affecting its convergence stability. This is often attributed to high sparsity of the implicit feedback and discrete values characteristic from items recommendation. To face these challenges, we propose a novel model named convolutional adversarial latent factor model (CALF), which uses adversarial training in generative and discriminative models for implicit feedback recommendations. We assume that users prefer observed items over generated items and then apply pairwise product to model the user-item interactions. Additionally, the latent features become input data of our convolutional neural network (CNN) to learn correlations among embedding dimensions. Finally, Rao-Blackwellized sampling is adopted to deal with the discrete values optimizing CALF and stabilizing the training step. We conducted extensive experiments on three different benchmark datasets, where our proposed model demonstrates its efficiency for item recommendation.

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the ``long tail'' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.

The Preferences for Everyday Living Inventory (PELI) is a 72-question instrument used for helping nursing homes assess person-centered care. In particular, the approach allows residents to express their preferences for both care and activities in order to provide direct care workers with insights on how to best provide a high-quality living experience. Among the challenges of using the PELI is its length: 72 questions give rise to issues of survey fatigue while also creating a workflow bottleneck for those providing care. In this paper we explore and evaluate the use of three different recommender strategies that we have applied to the PELI. In particular, we present the use of both rule-based and neighborhood-based collaborative filtering in order to make recommendations on which preference questions to present to a resident. We illustrate the approaches by providing a domain-specific example, and then compare the approaches across a number of performance and quality metrics.

Google is facing an onset of privacy concerns after it announced it plans to dissolve the Nest brand in favor of a new, all-purpose smart home division, called Google Nest. As part of the decision, existing users of Nest smart thermostats, security cameras and other products will be forced to migrate their information over to a Google account. The announcement, made at Google's I/O developer conference last week, has caught the eye of some users and experts who say it gives them little control over the future of their Nest data and, as a result, their privacy. When Google acquired Nest in 2014 for $3.2 billion, Nest pledged to keep the data it collects on its users separate from Googles other services. 'When you work with Nest and use Nest products, that data does not go into the greater Google or any of its other business units,' Tony Fadell, former CEO of Nest, told BBC in 2015.

Alexa's got a new gig: home safety. Amazon today introduces Alexa Guard, a free feature that uses the microphones of the Amazon Echo speakers to listen for the sounds of smoke and carbon monoxide alarms or breaking glass. Should the personal assistant hear any of those, it will send text alerts to your phone. Amazon says it's introducing the feature to "help you keep your home safe." Amazon says that security devices, such as the Ring video doorbell, which Amazon owns, or ADT Pulse, which it doesn't, can work with Guard in that Alexa will send the alerts to the security provider. Additionally, Alexa can be programmed to have smart, connected lights turn on and off remotely when you're out "to make it look like you're home while you're away," says Amazon.

The Like other social systems, in collaborative filtering a small number main tenet of this approach is to recommend items of interest to a of "influential" users may have a large impact on the recommendations user based on the preferences of other similar users in the system. of other users, thus affecting the overall behavior of the Because of the social nature of these systems, a small group of system. Identifying influential users and studying their impact on "influential" users can have a significant impact on the behavior other users is an important problem because it provides insight of the system towards other users. This type of influence may, in into how small groups can inadvertently or intentionally affect the some cases, result in undesirable effects such as bias toward certain behavior of the system as a whole. Modeling these influences can items, lack of diversity or imbalance in recommendations, and even also shed light on patterns and relationships that would otherwise potential security concerns such as making it easier to deliberately be difficult to discern, hopefully leading to more transparency in manipulate the system outcomes.