Between cat videos and online banking, the web runs the gamut of wasteful and wonderful when it comes to getting things done. And if you were to weigh the Internet's time wasters against its time-saving tools, it would be like an elephant sitting across a fulcrum from a field mouse. But through all the advances that have come with cloud computing, the Internet is quickly turning into a giant productivity machine. Shopping is great when you're soaking in some retail therapy, but when you're doing it because you have to, it can feel like a major chore. Mezi, a free iOS app, works like a personal assistant that you can text message. Just tell the service what you want--airfare, clothing, dinner reservations, personal electronics--and Mezi's agents scour the web for the best deals, even using coupons if possible to get you a good deal.

Virtual assistants are supposed to be a linchpin of tech's future. Apple, Google, Amazon, Microsoft, Samsung, and others have all made big investments in the tech. Many of them are using the idea of a disembodied, information-fetching helper as a selling point for new devices. But while these assistants are infiltrating more and more gadgets, they still face a few giant hurdles on their way to wider acceptance. One, as this chart from Statista shows, is simple: People just don't want to use them in public.

Prediction 1: Superhero CMOs Emerge - By 2020, the first superhero CMOs will emerge because they received C-Level permission to disrupt traditional go-to-market operations. IDC predicts that we will see pockets of break-through in CMO leadership. This will be demonstrated by individuals who have exceptional leadership skills and in the face of long-odds, have brought meaningful change to their marketing organizations. The "Superhero CMO" will be one who has executed real change -- not just the aspirational change that is depicted on a PowerPoint slide. Prediction 2: Boardroom Battle for the Customer - By 2020, 25% of CEO's will appoint a Chief Customer Officer (CCO) in an attempt to unify the imperative of customer-centricity.

The original idea behind Ask Jeeves was to provide answers to questions posed in everyday language. "Jeeves" was the name of your very own personal assistant, or butler, fetching answers to any question you asked. In a time where the internet was still very much in its infancy, the prospect really sparked my imagination. Excited like many others after seeing the advert, I turned on my PC to ask Jeeves a question. Although I don't remember the first question I asked, I do remember the disappointment I had when the answer was just a list of websites related to keywords in my search criteria.

This position paper describes an AI application for game spectators, e.g., those watching Twitch. The aim of this application is to automatically generate game plays by nonplayer characters -- not human players -- and recommend those plays to spectators. The generation part leads to development of a new field: procedural play generation (PPG). The recommendation part requires new techniques in recommender systems (RS) for incorporation of play content into RS to obtain promising recommendation results. Rather than proposing solutions to all relevant topics, this paper aims at drawing attention to this new field and serves as a seed for discussion and collaboration among the readers, workshop participants, and authors.

Many modern recommender systems rely on matrix factor-ization techniques to produce personalized recommendationson the basis of the feedback that users provided on differ-ent items in the past. The feedback may take different forms,such as the rating of a movie, or the number of times a userlistened to the songs of a given music band. Nonetheless, insome situations, the user can perform several actions on eachitem, and the feedback is multidimensional (e.g., the user ofan e-commerce website can either click on a product, add theproduct to her cart or buy it). In this case, one can no longerview the recommendation problem as a matrix completion,unless the problem is reduced to a series of multiple inde-pendent problems, thus loosing the correlation between thedifferent actions. In this case, the most suitable approach is touse a tensor approach to learn all dimensions of the feedbacksimultaneously. In this paper, we propose a specific instanceof tensor completion and we show how it can be heavily par-allelized over both the dimensions (i.e., items, users, actions)and within each dimension (i.e., each item separately). Wevalidate the proposed method both in terms of prediction ac-curacy and scalability to large datasets.

Collaborative filtering (CF) with implicit feedback is a successful method for recommending items to users, which does not require a knowledge of the items or users. CF methods can be mainly classified into two categories. One is point-wise regression based and the other is pair-wise ranking based, where the latter one only tries to find out the items that users prefer while ignores the items that users dislike, and usually gives out a better recommended item list. The performance of CF-based methods degrades significantly when the feedback information is sparse. To address the problem, many kinds of auxiliary information have been utilized such as users’ reviews on items, items’ content and description information, price, brands. In this paper we utilize a weighted recursive autoencoder (RAE) to extract useful features from several heterogeneous auxiliary information and tightly couple the weighted RAE with a pair-wise ranking based CF method. Analysis of the hyperparameters illustrates that auxiliary information from different sources is indeed able to benefit our model. Empirical experiments on six real world datasets show that our method outperforms other state-of-the-art methods.

Started from the Bumble now we here.

With the Super Bowl upon us once again, millions of people will gather with friends and family around big-screen TVs--and monumental levels of finger food--to watch the Atlanta Falcons take on the New England Patriots. More than ever, tech this year is changing the way we enjoy the big game. From virtual reality to artificial intelligence to 8K cameras and smartphone selfies, here's a guide to tech built just for the big event.

Amazon's Echo has been the hot tech gadget to own since it first launched more than two years ago. The home speaker pairs with a digital assistant, Alexa, allowing users to play music, search recipes or check the weather through simple voice commands. As a parent, the Echo can be your best friend. It tracks to-do-lists, what you need at the grocery store, and can even help with homework. For example, one six-year-old in Texas was chatting with Alexa, and ended up buying four pounds of cookies and a dollhouse worth $170.