Early Black Friday deals are already rolling out, with more new sales showing up every day. For 2021, there's concern whether shipping issues will mean less stuff on the shelves for the holidays. As we've already been hearing for months, the global supply chain is seriously backed up with bottlenecks occurring at key points. Hoping to deal with an already difficult situation, retailers stretched out the shopping season as much as they dared, what with shipping timelines stretching to frustrating levels. These supply problems are supposed to be felt acutely in technology, or so the market watchers say.

Designing recommendation systems with limited or no available training data remains a challenge. To that end, a new combinatorial optimization problem is formulated to generate optimized item selection for experimentation with the goal to shorten the time for collecting randomized training data. We first present an overview of the optimized item selection problem and a multi-level optimization framework to solve it. The approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. We then discuss how to incorporate optimized item selection with active learning as part of randomized exploration in an ongoing fashion.

The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score $s_p$ and minimize the negative sample score $s_n$, which can usually be summarized into two paradigms: the pointwise and the pairwise. The pointwise approaches fit each sample with its label individually, which is flexible in weighting and sampling on instance-level but ignores the inherent ranking property. By qualitatively minimizing the relative score $s_n - s_p$, the pairwise approaches capture the ranking of samples naturally but suffer from training efficiency. Additionally, both approaches are hard to explicitly provide a personalized decision boundary to determine if users are interested in items unseen. To address those issues, we innovatively introduce an auxiliary score $b_u$ for each user to represent the User Interest Boundary(UIB) and individually penalize samples that cross the boundary with pairwise paradigms, i.e., the positive samples whose score is lower than $b_u$ and the negative samples whose score is higher than $b_u$. In this way, our approach successfully achieves a hybrid loss of the pointwise and the pairwise to combine the advantages of both. Analytically, we show that our approach can provide a personalized decision boundary and significantly improve the training efficiency without any special sampling strategy. Extensive results show that our approach achieves significant improvements on not only the classical pointwise or pairwise models but also state-of-the-art models with complex loss function and complicated feature encoding.

We've all seen how artificial intelligence has established itself in a variety of human activities. As a result, the technology has received widespread recognition from industry players, investors, and businesses that have begun to equip their machines with intelligence, deploy automation and employ robots to perform tasks. A.I. is all around us, even if you aren't aware of it. Your insurer may be calculating your insurance using AI-powered policy software. Likewise, your bank is most likely assessing the risk of you defaulting on a loan.

In last 2–3 decades lots of technologies and computer languages discovered for the purpose of research, better learning and for more opportunities in Machines and Computing fields. One of the language on those discoveries are Machine Learning. The study of machine learning (ML) is the study of algorithms that develop automatically through different experiences and by accumulating data. Machine Learning is a branch of AI(artificial Intelligence). Artificial intelligence (AI) and computer science have developed machine learning techniques to reproduce how humans learn by using data and algorithms.

If we think of the most successful and widespread applications of machine learning in business, one of the examples would be recommender systems. Each time you visit Amazon or Netflix, you see recommended items or movies that you might like -- the product of recommender systems incorporated by these companies. Though a recommender system is a rather simple algorithm that discovers patterns in a dataset, rates items, and shows the user the items that they might rate highly, they have the power to boost sales of many e-commerce and retail companies. In simple words, these systems predict users' interests and recommend relevant items. User-item interactions -- the information about ratings, number of purchases, likes, and so on.

This November, the Smithsonian's FUTURES festival, featuring innovations that are set to change the world, will include a familiar face. Or, rather, voice: Q, introduced in 2019 as the first "genderless AI voice," is a human voice for use in digital assistants specifically created to be gender-ambiguous. "Q was designed to start a conversation around why we gender technology when technology has no gender to begin with," says Ryan Sherman, one of Q's co-creators. To design the voice, a team of linguists, sound engineers, and creatives collaborated with nonbinary individuals and sampled different voices to land on a sound range they felt had the potential to disrupt the status quo and represent nonbinary people in the world of AI. When Q was announced several years ago, it was hailed as "the genderless digital voice the world needs right now," and an acknowledgment of the harm of feminizing assistants, which perpetuates misogynistic stereotypes of women as submissive and obedient.

I came into the kitchen recently to find my husband cradling our electricity smart meter with the kind of tender attention more usually directed to a new-born, his phone clutched in his free hand. "You didn't turn your office heater off last night," he said. I went in this morning to turn it on again!" "Last night we used 10…" (here he added a unit, presumably of electricity, but all that stuff is Martian to me. "It shouldn't be that high." "But I turned it off!" But our smart home had spoken and it is far more reliable than me, his life partner of 26 years. Our house now has app-enabled devices to control the heating and the boiler remotely, to check temperature, CO2 and noise levels and to see who is at the door. There are motion-detector cameras in the garden that send us videos of foxes threatening my hens, or his tortoises escaping. Since we installed a few solar panels, my husband's smart-home management has become more urgent and more granular. An app tells him how much we are consuming, but also how much we are producing, in real time. Now he bursts in when it's sunny, shouting "We're giving electricity to the grid!

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work propose to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin.

Audio glasses, which have integrated microspeakers and a Bluetooth connection, are multiplying. Bose is leading the way with its Frames audio sunglasses. Amazon is also in the Bluetooth-glasses game with its Echo Frames, now on their second generation. A host of other companies, many of which are no-name Chinese manufacturers, have released audio glasses in recent months. Some are geared toward everyday use, allowing you to forgo headphones and stealthily listen to audio on the go, while others are designed for runners and bikers who want to leave their ears open to the world for safety reasons.