The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approach

In this paper, we propose a new low-rank matrix factorization model dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix $X$ and a factorization rank $r$, BSSMF looks for a matrix $W$ with $r$ columns and a matrix $H$ with $r$ rows such that $X \approx WH$ where the entries in each column of $W$ are bounded, that is, they belong to given intervals, and the columns of $H$ belong to the probability simplex, that is, $H$ is column stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and simplex-structured matrix factorization (SSMF). BSSMF is particularly well suited when the entries of the input matrix $X$ belong to a given interval; for example when the rows of $X$ represent images, or $X$ is a rating matrix such as in the Netflix and MovieLens datasets where the entries of $X$ belong to the interval $[1,5]$. The simplex-structured matrix $H$ not only leads to an easily understandable decomposition providing a soft clustering of the columns of $X$, but implies that the entries of each column of $WH$ belong to the same intervals as the columns of $W$. In this paper, we first propose a fast algorithm for BSSMF, even in the presence of missing data in $X$. Then we provide identifiability conditions for BSSMF, that is, we provide conditions under which BSSMF admits a unique decomposition, up to trivial ambiguities. Finally, we illustrate the effectiveness of BSSMF on two applications: extraction of features in a set of images, and the matrix completion problem for recommender systems.

Workers are being bombarded with data from a variety of sources. If the point is to make people more productive, as the volume increases, the more difficult it is to find the information you need to do your job. AI thrives in large data environments and can help cut through the noise, move across applications and find the nuggets that matter most. That's the idea behind Jigso, an early-stage startup that's building an AI assistant to act as an observability layer, not unlike security or performance monitoring, but instead of looking at logs, it's looking at the apps employees are using to find the information they need. Today the startup announced a $7.5 million seed investment.

Google is reshuffling the reporting structure of its virtual assistant unit -- called Assistant -- to focus more on Bard, the company's new artificial intelligence chat technology. In a memo to employees on Wednesday, titled "Changes to Assistant and Bard teams," Sissie Hsiao, vice president and lead of Google Assistant's business unit, announced changes to the organization that show the unit heavily prioritizing Bard. Jianchang "JC" Mao, who reported directly to Hsiao, will be leaving the company for personal reasons, according to the memo, which was viewed by CNBC. Mao held the position of vice president of engineering for Google Assistant and "helped shape the Assistant we have today," Hsiao wrote. Taking Mao's place will be 16-year Google veteran Peeyush Ranjan, who most recently held the title of vice president in Google's commerce organization, overseeing payments.

Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave differently. In such situations, most existing Click-Through Rate (CTR) models can't generalize well to online serving due to distribution uncertainty of the upcoming occasion. In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions. Firstly, we design a time series that consists of occasion signals generated from the online business scenario. Since occasion signals are more discriminative in the frequency domain, we apply Fourier Transformation to sliding time windows upon the time series, obtaining a sequence of frequency spectrum which is then processed by Occasion Evolution Layer (OEL). In this way, a high-order occasion representation can be learned to handle the online distribution uncertainty. Moreover, we adopt multiple experts to learn feature representations from multiple aspects, which are guided by the occasion representation via an attention mechanism. Accordingly, a mixture of feature representations is obtained adaptively for different occasions to predict the final CTR. Experimental results on real-world datasets validate the superiority of MOEF and online A/B tests also show MOEF outperforms representative CTR models significantly.

Interactions provides Intelligent Virtual Assistants that seamlessly assimilate conversational AI and human understanding to enable businesses to engage with their customers in highly productive and satisfying conversations. With flexible products and solutions designed to meet the growing demand for unified, optichannel customer care, Interactions is delivering unprecedented improvements in the customer experience and significant cost savings for some of the largest brands in the world. The company recently launched Trustera, a real-time, audio-sensitive redaction platform. Trustera preemptively identifies and protects sensitive information like credit card numbers and solves the biggest compliance challenge in today's contact-center environment: protecting a customer's Payment Card Information (PCI) anywhere it appears during a call. The platform is designed to make the customer experience more trustworthy, secure and seamless.

One of the biggest differences between online dating and the old-fashioned sort is the size of the pool. The number of people using dating apps dwarfs offline social networks. So sites offer filters that let users exclude unwanted groups. Your browser does not support the audio element. The diversity of tastes among giant user bases should make apps a haven for people who struggle with dating offline.

SHOPPING – Contains affiliated content. Products featured in this Mail Best article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. If you've been holding off investing in the best smart home devices as they tend to be expensive, Amazon is running an impressive deal right now that allows you to get a smart plug and an Echo Dot (5th Gen) for just £41.99. Amazon has blessed us with a new shopping event, the Amazon Spring Sale, ending just before midnight tonight, there are still tons of Amazon devices on sale right now - and you don't have to be a Prime member.

Abstract: As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users.

Abstract: Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this old' question and systematically study MoRec from several aspects.