The way we express ourselves, either orally or in writing, tells a lot about who we are and how we feel at that moment. The words, tone, pitch, everything adds up to create a piece of information that we want to convey to the other person and the same is very peculiar to us. In theory, human behaviour is predictable with the help of the information a human being conveys through verbal communication. An individual is capable of generating thousands of sentences, and each sentence has its own level of complexity attached to it. Now imagine the level of complexity in analysing the language used by crores of people.

What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. Google: Search results They are why Google is the most successful technology company today. YouTube: Video dashboard I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

A new British dating app is promising to eradicate Tinder Swindler-style romance fraud, which cost duped daters almost £100m last year, by ensuring that all members complete biometric ID verification before they digitally mingle. Fluttr, which claims to be the first UK online dating app to use such technology to improve user safety, is launching on Valentine's Day in the hope of getting a boost from singletons looking to change their relationship status. The issue of romance fraud, catfishing and fake profiles has been put into the spotlight following the release of Netflix documentary The Tinder Swindler, which tells the true story of a man who went to extraordinary lengths to scam women for millions after meeting them online. The pandemic, when online dating was the only mixing that was allowed, has driven a huge surge in scams costing those duped £92m in the UK last year. "We want to rid the world of Tinder Swindlers and create a safe space free from the fake profiles used to defraud, catfish and abuse online daters," said Rhonda Alexander, the chief executive and co-founder of Fluttr.

E-commerce platforms generate vast amounts of customer behavior data, such as clicks and purchases, from millions of unique users every day. However, effectively using this data for behavior understanding tasks is challenging because there are usually not enough labels to learn from all users in a supervised manner. This paper extends the BERT model to e-commerce user data for pre-training representations in a self-supervised manner. By viewing user actions in sequences as analogous to words in sentences, we extend the existing BERT model to user behavior data. Further, our model adopts a unified structure to simultaneously learn from long-term and short-term user behavior, as well as user attributes. We propose methods for the tokenization of different types of user behavior sequences, the generation of input representation vectors, and a novel pretext task to enable the pre-trained model to learn from its own input, eliminating the need for labeled training data. Extensive experiments demonstrate that the learned representations result in significant improvements when transferred to three different real-world tasks, particularly compared to task-specific modeling and multi-task representation learning

In efforts to introduce authenticity to online dating, Tinder has rolled out a new feature called "Blind Date," which pairs swipers up for a chat before they're able to view each other's profiles. "Inspired by the OG way to meet someone new, usually at the hand of a meddlesome aunt or well-meaning friend, Blind Date gives the daters of today a low-pressure way to put their personality first and find a match they truly vibe with," Tinder said. People on Tinder who try out this feature will answer a series of icebreaker questions like "It's OK to wear a shirt ____ times without washing it" and "I put ketchup on____." Based on their responses, users will get matched and see the responses of their potential match. They will then get placed in a timed chat, after which they can choose to match with the other person or not.

Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency, among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies for them according to the evaluation perspectives and evaluation methods. Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them. The contents of this survey are based on more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP, and IUI, and their complete summarization are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.

Learn how to build machine learning recommender systems from one of Amazon's pioneers in the field. Updated with Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs) Learn how to build machine learning recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.

Artificial intelligence (AI) has been invading our daily lives without us fully realizing it. When you wake up in the morning, you may ask Alexa to give you a run-down of your daily schedule. When you drive to work using Waze, the app is using a machine-learning algorithm to provide the best route for you. When you watch a movie or a show on Netflix or make a purchase on Amazon, the platforms use AI to make content or product recommendations for you. If Netflix can use AI to make content recommendations for us, businesses and enterprises can also use AI to make personalized content recommendations when prospects visit their websites.

BARCELONA, CATALONIA, SPAIN - 2019/02/27: The humanoid robot Pepper of the American company ... [ ] CloudMinds is seen shaking hands with a visitor during the MWC2019. The MWC2019 Mobile World Congress opens its doors to showcase the latest news of the manufacturers of smart phones. The presence of devices prepared to manage 5G communications has been the hallmark of this edition. Robots are stealing our jobs! AI is making us obsolete!

Investors looking to diversify their portfolios and coin collectors looking to add a new treasure to their collection are familiar with the benefits and value that precious metals can offer. To help make the purchasing process easier, IBM worked with TD Securities to launch an AI-based virtual assistant powered by IBM Watson Assistant that can help customers with inquiries on the TD Precious Metals digital store, including frequently asked questions. The TD Precious Metals digital store allows customers to buy physical gold, silver and platinum bullion and coins online from the comfort of their home. The new virtual assistant, now available as a feature on the TD Precious Metals digital store, provides customers with a convenient self-service option, available 24/7, for frequently asked questions about TD Precious Metals. Customers type their questions into the virtual assistant and receive an instant written response, along with links to help further assist them.