Emerging technology trends clearly point to a future encompassing screen-less interactions between businesses and consumers, with voice, augmented and virtual reality, wearable devices, and artificial intelligence, gradually but definitely removing the traditional graphic user interface (GUI) from the equation. The next decade is expected to be even more disruptive based on the methodologies used by customers to interact with brands. A closer glimpse of the consumer landscape, reveals irrefutable enthusiasm for artificial intelligence (AI) as compared to other upcoming technologies. However, the technology is still in the experimental phase. Even though the majority of enterprise leaders consider AI to be a business advantage, many organizations are taciturn to trust AI to the extent of deferring implementation and hence are yet to benefit from the technology's promising capabilities.

In the documentary Swiped, filmmaker Nancy Jo Sales investigates how dating apps have created unintended consequences in actual relationships. In the documentary Swiped, filmmaker Nancy Jo Sales investigates how dating apps have created unintended consequences in actual relationships. For some of the 40 million or so Americans who currently use online dating apps like Tinder, Bumble and Hinge, the findings of the new HBO documentary Swiped might be intuitively obvious. But for others, there may still be revelations aplenty in the film, which is subtitled Hooking Up in the Digital Age. It's about how these apps may change how we think about relationships -- and it doesn't paint a positive picture.

Comedian Norm MacDonald just co-created a new video dating app, LOKO, but he's not ready to put himself out there on it. "I am not using it," he told USA TODAY this week. MacDonald is best known for his years on Saturday Night Live and sitcoms like The Norm Show. He's got a new gig for Netflix, a talk show called Norm MacDonald Has a Show, debuting on Sept. 14th. The LOKO app came out of a corporate gig, where he met Canadian entrepreneur Vivek Jain, who complained about his lack of success finding dates.

What features are you hoping to see in the new iPhone? USA TODAY's Jefferson Graham provides his wishlist for Tim Cook and Apple. We've got the kickoff to the gadget-selling season Wednesday with the introduction of new iPhones and possibly more Apple products. I love to geek out as much as the next guy, so thanks for inviting me. You've got quite a sales job ahead of you, though.

Jefferson Graham offers tips on how to listen to the Talking Tech podcast via apps, speakers, TVs and the car. The Talking Tech podcast is available for you every day with a quick hit on the latest tech news, gadget reviews, opinion on tech trends and interviews with insiders. On this page, you'll find quick links to all of our shows. In September so far, we've covered everything from how to watch NFL football games on streaming, new tools added to Amazon's Echo to offer voice-activated concert and touring information and our tips on why you shouldn't buy a new or older iPhone until after September 21st. We had comedian Norm MacDonald visit Talking Tech to tell about the new dating app he created with a friend.

While Google has yet to launch its digital wellbeing features on Android phones, it's already thinking of how to bring them to the smart speaker in your living room. The 9to5Google team has discovered material in the the latest Google app for Android that points to these digital health features coming to Home speakers and other Assistant-equipped devices. While the exact functionality isn't clear, there's a Downtime feature that could stop people from using Home at certain times of the day, such as when they're winding down at the end of the night. That could be particularly helpful if you have a bad habit of playing music or asking questions when you should be getting to bed. "Filters," meanwhile, appears set to replace Family Mode and may limit what kids are allowed to search for, or prevent them from playing some voice-based games.

Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by transforming users' implicit feedback towards auxiliary domain items into an embedding in the target domain item embedding space. ImplicitCE learns these embedding spaces and transformation function in an end-to-end fashion and can co-embed users and items with any differentiable similarity function. To train ImplicitCE we explore methods for maximizing the correlations between model predictions and users' affinities and introduce Sample Correlation Update, a novel and extremely simple training strategy. Finally, we show that ImplicitCE trained with Sample Correlation Update outperforms a variety of state of the art algorithms and loss functions on both a large scale Twitter dataset and the DBLP dataset.

Since 2010, we have built and maintained LensKit, an open-source toolkit for building, researching, and learning about recommender systems. We have successfully used the software in a wide range of recommender systems experiments, to support education in traditional classroom and online settings, and as the algorithmic backend for user-facing recommendation services in movies and books. This experience, along with community feedback, has surfaced a number of challenges with LensKit's design and environmental choices. In response to these challenges, we are developing a new set of tools that leverage the PyData stack to enable the kinds of research experiments and educational experiences that we have been able to deliver with LensKit, along with new experimental structures that the existing code makes difficult. The result is a set of research tools that should significantly increase research velocity and provide much smoother integration with other software such as Keras while maintaining the same level of reproducibility as a LensKit experiment. In this paper, we reflect on the LensKit project, particularly on our experience using it for offline evaluation experiments, and describe the next-generation LKPY tools for enabling new offline evaluations and experiments with flexible, open-ended designs and well-tested evaluation primitives.

In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigation by showing items. Previous works have addressed this recommendation setup through the task of predicting the next item user will interact with. In particular, Recurrent Neural Networks (RNNs) has been shown to achieve substantial improvements over collaborative filtering baselines. In this paper, we consider interactions triggered by the recommendations of deployed recommender system in addition to browsing behavior. Indeed, it is reported that in online services interactions with recommendations represent up to 30\% of total interactions. Moreover, in practice, recommender system can greatly influence user behavior by promoting specific items. In this paper, we extend the RNN modeling framework by taking into account user interaction with recommended items. We propose and evaluate RNN architectures that consist of the recommendation action module and the state-action fusion module. Using real-world large-scale datasets we demonstrate improved performance on the next item prediction task compared to the baselines.

Conditional preference networks (CP-nets) are a graphical representation of a person's (conditional) preferences over a set of discrete variables. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user's preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user's preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. Through experimental results, we show that this method is more effective than existing techniques for improving dominance testing efficiency. We show that the above results also hold for CP-nets that express indifference between variable values.