One of the most common challenges in an e-commerce business to build a well-performing product recommender and categorisation model. A product recommender is used to recommend similar products to users so that total time and money spent on platform per user will be increased. There is also a need to have a model to categorise products correctly since there might be some wrongly categorised products in those platforms especially where most of content is generated by users as in case of classified websites. A product categorisation model is used to catch those products and place them back into their right categories to improve overall user experience on the platform. This article has 2 main parts.

Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' preferences are directly shaped by what content they see -- radicalization, rabbit holes, polarization, and boredom are all example phenomena of preferences affected by content. Polarization in particular can occur even in ecosystems with "mass media," where no personalization takes place, as recently explored in a natural model of preference dynamics by~\citet{hkazla2019geometric} and~\citet{gaitonde2021polarization}. If all users' preferences are drawn towards content they already like, or are repelled from content they already dislike, uniform consumption of media leads to a population of heterogeneous preferences converging towards only two poles. In this work, we explore whether some phenomenon akin to polarization occurs when users receive \emph{personalized} content recommendations. We use a similar model of preference dynamics, where an individual's preferences move towards content the consume and enjoy, and away from content they consume and dislike. We show that standard user reward maximization is an almost trivial goal in such an environment (a large class of simple algorithms will achieve only constant regret). A more interesting objective, then, is to understand under what conditions a recommendation algorithm can ensure stationarity of user's preferences. We show how to design a content recommendations which can achieve approximate stationarity, under mild conditions on the set of available content, when a user's preferences are known, and how one can learn enough about a user's preferences to implement such a strategy even when user preferences are initially unknown.

Robots are not limited to industrial works anymore! Thanks to the integration of artificial intelligence and voice recognition, robots are slowly invading our smart homes embedded with devices like wireless security cameras, Smart TVs, Amazon's Alexa, Amazon Echo, Google Assistant, Philips Hue lightbulbs, Ecobee4, etc. And it is not a secret that machine learning software development is on rise now. A lot of clients are coming to develop personalized ML solutions for their businesses. ABI Research predicts that this integration will grow, and by 2024 that over 79 million homes in the world will have a robot in the house.

While dating apps were once seen as taboo, they're now one of the main ways that singletons find love around the world. But if you have a profile on a dating app, a new study may encourage you to reassess which pictures you include. Researchers from the University of Colorado have revealed that men who pose topless on Tinder are seen as less competent and more promiscuous. The first dating app can be traced back to 1995 when Match.com was first launched. The website allowed single people to upload a profile, a picture and chat to people online.

It is utopian to rule out any form of anthropomorphism when addressing a conversational assistant because of the use of language as a vector of exchange. Designers, therefore, must limit these shortcomings with the implementation of these design rules, thus reducing the risks of deception and dependency, and giving confidence in these systems.

Galaxy Watch 4 users can now start using Google Assistant on the device. Along with being available in the app tray, you can trigger Assistant via voice command and assign it to a long press on the home button. In addition, today's update offers users access to Google Pay, Maps and YouTube Music. During Google I/O earlier this month, Samsung said Google Assistant support would arrive on the device sometime this summer, so it's arriving earlier than some may have expected. Patrick Chomet, Samsung's executive vice president of products and experience, noted that Assistant would allow for "faster and more natural voice interactions."

Artificial Intelligence was first popularized by a small group of scientist gathered at the Dartmouth College in the United States in 1956. Since then, AI has advanced considerably and is powering many real-world applications ranging from facial recognition to language translators and virtual assistants such as Siri and Alexa. Still, we are far from witnessing AI-powered robots emulating humans. So far that is confined to the Sci-Fi movies. However, AI has created quite a stir in the business world with its many benefits and challenges.

Mobile artificial intelligence is disrupting the already breakneck-paced mobile app development game. In 2020, the mobile AI sector reached a valuation of 2.14 billion dollars, and that number is expected to grow 4.5x by the year 2026. It's safe to say that mobile artificial intelligence is here to stay, so let's find out how this innovative technology is used in mobile app development. Mobile artificial intelligence aims at making mobile technology smarter and more functional for users. A well-known example of the power of mobile AI is Amazon's Alexa Shopping product, which has freed up countless hours of customer support grunt work for Amazon.

Our advice columnists have heard it all over the years. Each Sunday, we dive into the Dear Prudie archives and share a selection of classic letters with our readers. For the past few months, my mom has been catfishing a guy online and I don't know what to do. Earlier this year, I decided to give online dating a try and signed up for a free online dating site. My mom was very supportive and interested in me finding someone, and, unbeknownst to me, created a fake profile to scope out the site.

The age of artificial intelligence (A.I.) is finally upon us. Consumer applications of A.I., in particular, have come a long way, leading to more accurate search results for online shoppers, allowing apps and websites to make more personalized recommendations, and enabling voice-activated digital assistants to better understand us. We all know there is tremendous potential value in data, which continues to grow exponentially. In fact, the world is creating 2.5 quintillion bytes of data every day (that's 2.5 followed by 18 zeros). To harness that potential, companies need A.I. to make sense of the data, and hybrid cloud computing platforms that can distribute it across organizations.