Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms (e.g. Taobao). Generally speaking, two essential modules named "Click-Through Rate Prediction" (CTR) and "Conversion Rate Prediction" (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multi-Level Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model's diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pre-trained GBDT models. Experimental results on off-line dataset and online deployment demonstrate the effectiveness of the proposed methods.

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.

Though not one to give much away, Apple's voice assistant Siri has hinted she may be undergoing a major upgrade ahead of the technology company's annual developer conference. Australian site the Apple Post noted that if a user asks Siri about WWDC, she won't provide any useful information but will instead drop cryptic hints.

As digital assistants improve, we're learning new things to expect from them, but the tasks that a real-life assistant may have handled before can still be a bit of a challenge to home assistants. Amazon's Alexa voice assistant is gaining functionality to help it get smarter about working with your calendar. The new abilities will let users move appointments around and schedule meetings based on other people's availability. If you've been shared on someone's calendar availability, Alexa will be able to suggest times that work for both of you. Just say, "Alexa schedule a meeting with [name]" and Amazon's assistant will search through your schedule for a good time, suggesting up to two time slots that could work.

We're already used to virtual assistance like Siri and Alexa, and with more sophisticated forms of artificial intelligence these virtual assistants will be …

By Will Yang Technology in today's day and age has enabled a human to do things and accomplish far more than one could think of a few years back. Thanks to rapidly evolving and innovative technologies, personal lives have become more enriched. Meaningful collaborations between a human and machine/technology has in many ways provided a wealth of opportunities to us making our lives comfortable. One such technology buzzword in the industry today is Artificial Intelligence. Once a topic for science fiction, Artificial Intelligence technology is now being used by brands across industries and categories.

From The Terminator to Blade Runner, pop culture has always leaned towards a chilling depiction of artificial intelligence (AI) and our future with AI at the helm. Recent headlines about Facebook panicking because their AI bots developed a language of their own have us hitting the alarm button once again. Should we really feel unsettled with an AI future? News flash: that future is here. If you ask Siri, the helpful assistant who magically lives inside your phone, to read text messages and emails to you, find the nearest pizza place or call your mother for you, then you've made AI a part of your everyday life.

As technology continues to grow, here are some ways you should start thinking about how automation and artificial intelligence will impact local businesses. Automation has already shaken up many blue collar industries, but it's set to change white collar industries as well. Revenue from artificial Intelligence software will grow from a $644 million in 2016 to nearly $39 billion in 2025, according to IBM. As this technology grows, there are many chances for technologists to implement AI into many facets of our lives and careers. We're already used to virtual assistance like Siri and Alexa, and with more sophisticated forms of artificial intelligence these virtual assistants will be able to give us direction on how to get to the store, while also telling us to get better exercise and other life tips.

Microsoft has acquired Semantic Machines, an artificial intelligence startup which could help Cortana hold more natural dialog with users. The California company has focused its efforts on so-called "conversational AI," moving beyond the more basic back-and-forth currently supported by the Google Assistant, Apple's Siri, and Amazon's Alexa. Right now, talking with one of those virtual helpers could leave you doubting the "intelligence" part of AI. A big part of that is their difficulty identifying meaning: effectively picking up on the thread of a conversation, and using that to understand different requests and commands. Semantic Machines is taking a different approach.

Artificial Intelligence (AI) is no longer just a fictional topic pitched around sci-fi fanatics. Our world is becoming more and more cohesive with AI. When you call customer service for your mobile related issues and interact with the automated voice, you're being served by AI. When you tell your Google Home, Amazon Echo, or even Siri to play your favorite playlist, you're interacting with AI. This smart technology is more concrete today than it ever was in the past, and the future of AI seems as bright as ever.