Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization is modeled using normal distributions. To find better probabilistic models, algorithms such as RankMat, ZeroMat and DotMat have been invented in recent years. In this paper, we model the user rating behavior in recommender system as a Poisson process, and design an algorithm that relies on no input data to solve the recommendation problem and the cold start issue at the same time. We prove the superiority of our algorithm in comparison with matrix factorization, random placement, Zipf placement, ZeroMat, DotMat, etc.

In our weekly recap on Sunday, we, as editors, look back at the past seven days. We do this at the suggestion of our cartoonist Albert Jan Rasker. He chooses a subject, makes a drawing, and we take it from there. If you'd like to receive this weekly recap directly in your mailbox every Sunday morning, just subscribe here. Artificial Intelligence (AI) has come to control our lives step by step.

Feeld Notes is a column about a middle-aged woman who suddenly realizes she wants to have sex again--and the beguiling app she uses to do it. Men are disappearing on me all the time on this fucking app. Within the first six weeks of my time on Feeld, I'd had some fun, but I'd also been flaked on or ghosted no fewer than six times. There was one guy, Mike, a short, brown-haired, 33-year-old writer who described himself as a "dom" and promised to show me the ropes around domination and submission. I spent a few steamy nights chatting with him on WhatsApp, then made a plan to meet up with him on a Friday night.

Artificial intelligence, or AI, is everywhere right now. In truth, the fundamentals of AI and machine learning have been around for a long time. The first primitive form of AI was an automated checkers bot which was created by Cristopher Strachey from the University of Manchester, England, back in 1951. It's come a long way since then, and we're starting to see a large number of high profile use cases for the technology being thrust into the mainstream. Some of the hottest applications of AI include the development of autonomous vehicles, facial recognition software, virtual assistants like Amazon's AMZN Alexa and Apple's AAPL Siri and a huge array of industrial applications in all industries from farming to gaming to healthcare.

Microsoft Cortana, we barely knew ye. A combination of inflationary effects of money printing through the pandemic, Russia's war of aggression in Ukraine, and even China's COVID-Zero lockdown policies -- are all contributing to a perfect economic storm hitting practically everyone. From gas prices to food costs, we're all tightening our belts, and for the world's biggest companies, that means accepting some harsh realities about some of their more experimental departments. Some of the biggest tech stories of this year revolved around Facebook's staggering fall from grace, with billions upon billions wiped off its market value owing to a smorgasbord of strategic and macroeconomic headwinds. The focus on its experimental metaverse saw the company rebrand to Meta and imagine a future where users sought to wear bulky VR computers on their faces to do basic tasks rather than the palm-sized pocket computers they already owned. Amazon, quite similarly, has come to terms with its own human-computer interfacing failure this year, although you probably wouldn't have realized it from how popular they are.

In this paper, we study item advertisements for small businesses. This application recommends prospective customers to specific items requested by businesses. From analysis, we found that the existing Recommender Systems (RS) were ineffective for small/new businesses with a few sales history. Training samples in RS can be highly biased toward popular businesses with sufficient sales and can decrease advertising performance for small businesses. We propose a meta-learning-based RS to improve advertising performance for small/new businesses and shops: Meta-Shop. Meta-Shop leverages an advanced meta-learning optimization framework and builds a model for a shop-level recommendation. It also integrates and transfers knowledge between large and small shops, consequently learning better features in small shops. We conducted experiments on a real-world E-commerce dataset and a public benchmark dataset. Meta-Shop outperformed a production baseline and the state-of-the-art RS models. Specifically, it achieved up to 16.6% relative improvement of Recall@1M and 40.4% relative improvement of nDCG@3 for user recommendations to new shops compared to the other RS models.

Centuries ago, the future of diplomacy was questioned due to technological progress – the invention of the radio, the telegraph and the public's intervention in the foreign policy domain. Meta's recent AI model has revived the conversation. CICERO has become the talk of the tech town and international chatbot diplomacy may not be far behind. Diplomacy is an art that even the most powerful AI was not able to master till CICERO made an appearance. Inspired by the famous Roman orator Cicero, Meta AI showed off its model that can beat several humans in the board game, Diplomacy, which requires strategic planning and verbal negotiations with several other players.

Artificial intelligence, or AI, is everywhere right now. In truth, the fundamentals of AI and machine learning have been around for a long time. The first primitive form of AI was an automated checkers bot which was created by Cristopher Strachey from the University of Manchester, England, back in 1951. It's come a long way since then, and we're starting to see a large number of high profile use cases for the technology being thrust into the mainstream. Some of the hottest applications of AI include the development of autonomous vehicles, facial recognition software, virtual assistants like Amazon's Alexa and Apple's Siri and a huge array of industrial applications in all industries from farming to gaming to healthcare.

Cyber Monday has come and gone, but if you're still looking to pick up a new smart display or two ahead of the holidays, a newer deal on Amazon's Echo Shows may be of interest: The retailer is currently offering a bundle that pairs its Echo Show 8 with the Kids edition of its Echo Show 5 for $70. We've seen the Echo Show 8 alone go for $70 for much of the last two months, but that still equals the lowest price we've tracked. Normally, it retails closer to $100. With this deal, you're effectively getting an Echo Show 5 Kids thrown in at no extra cost. That device is currently available on its own for $40, but its average street price over the last few months has sat closer to $60.

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market and accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results reveal that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.