AFTER A LONG and lonely lockdown, Theresa Causa was ready for love. To find it, the 40-year-old nurse practitioner in San Antonio turned to the new dating app "S'More," which helps users pair up by literally shifting the focus from physical appearances to mutual goals and interests. When matches first connect, they see only blurred versions of each other's profile photos, along with bios, hobbies and answers to prompts like "What are your top 3 qualities in a match?" As they exchange messages, their photos gradually un-blur. "I was, like, 'This is for me,'" said Ms. Causa.

Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user's trust in the recommendations, and the analyst's ability to scrutinize a model's outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. The result is a ranking model that aptly captures both debiased and explainable user preferences. Finally, we perform an empirical study on three real-world datasets that demonstrate the advantages of our proposed models.

In recent times, chatbots have become the user's preferred medium of communication. Conversational AIs or digital assistants elevate this medium by making user interaction more dynamic and personalized.

If you're hoping to be successful on a dinner date, be more adventurous when choosing from the restaurant menu, a new study suggests. Researchers in Pennsylvania have found that people who are open to try new foods are perceived as more sexually desirable and less sexually restricted. Meanwhile, a reluctance to try new foods – known as'food neophobia' – and sticking to the safe option on the menu is perceived as something of a turn-off. A willingness to engage in trying something new at the dining table could be a'cue' for a willingness to have an intimate experience with someone new as well, the experts suggest. Interestingly, this pattern is specific to willingness to try new foods, not general willingness to try other new things, like hobbies, music or TV shows, they report.

Google has banned "sugar dating" apps as part of a broad range of changes to its Play Store rules. Such apps allow people to find others to engage in "compensated sexual relationships", as Google puts it, wherein one party will pay someone to date them. Such apps and sites have become popular across the internet. There are a host of apps on the store with the name "Sugar Daddy", for instnace, and many more with more specific names. Many of them are explicit about what they are aiming to do, with one named Spoil offering to help people "find other wealthy sugar daddies who are looking for a discreet arrangement, and you can also meet other attractive girls who are looking for generous men", as PhoneArena notes.

Products featured in this Mail Best article are independently selected by our shopping writers. If you make a purchase using links on this page, we may earn an affiliate commission. The launch was originally announced back in September 2020, and the brand new Amazon Echo Dot Kids is now finally available to buy - and it's already being snapped up by eager parents. Amazon's most popular smart speaker has been given a makeover, and this new version is purposefully built with children in mind. Pitched as a fun way for kids to learn and have fun, it comes with a whole host of features that can help your little ones learn and play.

The term "machine learning" makes it sound like computers will solve problems for us without much human guidance. But, some fascinating careers are paving the way for artificial intelligence to help us all out in our daily lives and at work. Machine Learning Engineers and Data Scientists that specialize in machine learning get to work in pretty diverse industries. That's one of the best things about a career in programming or data science -- you can take those skills just about anywhere. It also means that you can work in a field that excites you or one in which you feel like you're making a positive contribution.

In 2021, Artificial intelligence (AI) is no longer merely a genre of cult Sci-Fi movies. Without many of us even noticing, AI has become an integral part of our everyday lives. Alexa, Siri, Cortana), we are surrounded by AI, even if we don't always realize it. In today's blog, let's review some of the most important examples of AI in business. Artificial intelligence is the "simulation of human intelligence," usually using some form of machine processing and learning designed to emulate the human brain.

From the approachable Geordie dialect to the instantly recognisable Liverpool lilt, many of England's most distinctive accents are from the north. But a new study has warned that northern accents could all but disappear in just 45 years. Using physics modelling, researchers from the Universities of Portsmouth and Cambridge predicted how accents are likely to change across England by 2066. Their findings suggest that northern accents could be replaced with'posh' south eastern pronunciations. However, certain north-south differences are predicted to remain - we will continue to disagree about the pronunciation of bath', according to the researchers.

Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.