Tech columnist Kim Komando shows you the settings you need to change on Amazon settings to safeguard your privacy. Amazon is not only watching over your shopping, TV viewing, music listening and book reading histories, it's also listening to you at home, or in the car. At least that's how it is in my household, where I have two Amazon Echo speakers – one in the kitchen and another in the garage, plus a car accessory to bring the Alexa personal assistant along with me on drives. I don't have a lot of smart home devices, but if I did, Amazon would have access to my doorbell and security – who's coming and going – and more. At the Amazon CES booth in 2019, the e-tailer showed off many products that work with Alexa. Unlike Facebook and Google, which slyly follow you around on your mobile phone and elsewhere to slip in more product sells, even if you're not using their apps, Amazon is rather upfront about the information it collects, even if it's hidden in several pages of a help menu.

"When you think about recommending something to someone, there's a real business reason why you might want to do that." Machine learning recommendation systems are not just a trendy feature of online stores. It is a mighty tool that can propel your business to the next level, if used strategically. No wonder Jack Chua suggests always having "a great tie-in to the underlying KPI of what you want to drive". If you're still hesitating on how exactly to use recommendations to invigorate your business, we invite you to learn from the experience of those who already made it work brilliantly! We collected the best examples of machine learning implementation in recommenders (including our own development projects) and explain in plain English how to build a machine learning recommender systems from scratch. Okay, let's start with a short quiz. Try to remember as many types of recommendation systems as you can.

Is your eCommerce business suffering from costly returns, customer churn, or margin decreases? Do you try your best to optimize prices but spend too much effort? Or maybe your online consultants are overloaded with claims and questions from customers and fail to deliver the best quality services? If you recognized some of the challenges of your business, then it may be useful for you to know how the most successful retailers in the world manage them with the help of Machine Learning. Come on and dive in with us into the ocean of the opportunities for your eCommerce business with ML! To start, I'll give you three examples of using Machine Learning for eCommerce: Big or small, most retail websites have similar challenges and goals.

We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adjusting the hyperparameter of the concave/linear composition to trade-off relevance and diversity, if any, one is in fact tuning the `total curvature' of the function for relevance-diversity trade-off.

Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent semantic relations between users and items. To address these concerns, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into low-dimensional discrete vectors. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures.

Within two years there will be more voice assistants on the internet than there are people on the planet. Another, possibly more helpful, way of looking at these statistics is to say that there will still be only half a dozen assistants that matter: Apple's Siri, Google's Assistant, and Amazon's Alexa in the west, along with their Chinese equivalents, but these will have billions of microphones at their disposal, listening patiently for sounds they can use. Voice is going to become the chief way that we make our wants known to computers – and when they respond, they will do so with female voices. This detail may seem trivial, but it goes to the heart of the way in which the spread of digital technologies can amplify and extend social prejudice. The companies that program these assistants want them to be used, of course, and this requires making them appear helpful. That's especially necessary when their helpfulness is limited in the real world: although they are getting better at answering queries outside narrow and canned parameters, they could not easily ever be mistaken for a human being on the basis of their words alone.

AI offers exceptional opportunities particularly in digital marketing while irrefutably revolutionizing and propelling the industry. AI is the ability of a computer or computer-enabled robotic systems to process massive amounts of in-depth data and produce outcomes similar to the thought processes of humans in learning, analysing, decision making, and problem-solving. Hence, AI has enabled marketers to comprehend vast data to gain valuable consumer insights, and in turn, improve digital marketing strategies. The applications of AI are essentially limitless, and the field of computer science is on a stark ascendance. The global AI market was worth $7.35 billion in 2018, where the largest portion of revenue was stirred from enterprise applications.

The field of artificial intelligence is exploding with projects such as IBM Watson, DeepMind's AlphaZero, and voice recognition used in virtual assistants including Amazon's Alexa, Apple's Siri, and Google's Home Assistant. Because of the increasing impact of AI on people's lives, concern is growing about how to take a sound ethical approach to future developments. Building ethical artificial intelligence requires both a moral approach to building AI systems and a plan for making AI systems themselves ethical. For example, developers of self-driving cars should be considering their social consequences including ensuring that the cars themselves are capable of making ethical decisions. Here are some major issues that need to be considered.

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When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries. However, to build such a travel agent is non-trivial. First of all, travel naturally involves several sub-tasks such as hotel reservation, restaurant recommendation and taxi booking etc, which invokes the need for global topic control. Secondly, the agent should consider various constraints like price or distance given by the user to recommend an appropriate venue. In this paper, we present a Deep Conversational Recommender (DCR) and apply to travel. It augments the sequence-to-sequence (seq2seq) models with a neural latent topic component to better guide response generation and make the training easier. To consider the various constraints for venue recommendation, we leverage a graph convolutional network (GCN) based approach to capture the relationships between different venues and the match between venue and dialog context. For response generation, we combine the topic-based component with the idea of pointer networks, which allows us to effectively incorporate recommendation results. We perform extensive evaluation on a multi-turn task-oriented dialog dataset in travel domain and the results show that our method achieves superior performance as compared to a wide range of baselines.