So let's dive into some specifics, shall we? In a nutshell, Augment adds numerous augments (get it?!) onto your existing devices. Those lowercase augments are best described as layers of intelligence that observe what you're doing and then step in as needed to make sure you always have the info you need exactly when you need it. If that concept rings a bell, congratulations: You've been paying attention. Philosophically, at least, Augment is strikingly similar to Heyday, a context-surfacing service I covered for Fast Company earlier this year.

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites. In the past few decades, the rapid developments of recommender systems have significantly benefited human by creating economic value, saving time and effort, and promoting social good. However, recent studies have found that data-driven recommender systems can pose serious threats to users and society, such as spreading fake news to manipulate public opinion in social media sites, amplifying unfairness toward under-represented groups or individuals in job matching services, or inferring privacy information from recommendation results. Therefore, systems' trustworthiness has been attracting increasing attention from various aspects for mitigating negative impacts caused by recommender systems, so as to enhance the public's trust towards recommender systems techniques. In this survey, we provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.

The global AI in retail market size is anticipated to reach USD 40.74 billion by 2030, expanding at a CAGR of 23.9% from 2022 to 2030, according to a new study by Grand View Research, Inc. The rising prominence of advanced technologies, such as chatbots and voice recognition programs, has furthered the growth potential. Moreover, the emerging online retail sales, increasing focus of retailers on improving customers' shopping experience, rising reliance on digital marketing, and growing investments in AI, accompanied by supportive government regulations, are the crucial factors contributing to the progress of the industry worldwide. Read 145 page full market research report for more Insights, "AI In Retail Market Size, Share & Trends Analysis Report By Component, By Technology (Chatbots, Natural Language Processing), By Sales Channel, By Application, By Region, And Segment Forecasts, 2022 - 2030", published by Grand View Research. AI algorithms play a pivotal role in assessing a considerable amount of data collated from consumers' online behavior.

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally, we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of-the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.

This article was published as a part of the Data Science Blogathon. However, we can improve the system's accuracy by leveraging contextual information. Any type of contextual information, like device context, conversational context, and metadata, such as the time the request was issued, etc., could be utilized to customize the underlying ASR model of the conversational agent (Alexa in our case). In this article, we will understand the need for contextual ASR and will also go over how the context vector is created in Alexa and the issues this process presents. Contextual information like the text of a user's request, the history of the user's recent interactions with a virtual assistant, and metadata like the time the request was issued can be used to customize the underlying ASR model to make a context-aware VA.

The concept of machine learning is essentially the same as what it sounds like; it refers to the concept that various forms of technology, such as computers and tablets, can learn something based on programming and other data. Although it has the appearance of an idea from the far future, most people now make regular use of this level of technology. One particularly useful application of this is speech recognition. The technology is utilized by virtual assistants such as Siri and Alexa to do tasks such as reciting reminders, answering queries, and carrying out requests. As machine learning becomes increasingly popular, an increasing number of individuals are deciding to specialize in the field as machine learning engineers.

Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It's a powerful tool for creating personalized and dynamic experiences, and it's already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it's clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round. As was the case with the mobile revolution, and the web before that, ML will cause us to rethink, restructure, displace, and consider new possibilities for virtually every experience we build. In the Google UX community, we've started an effort called "human-centered machine learning" (HCML) to help focus and guide that conversation.

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals.

Both in academic and industry-based research, online evaluation methods are seen as the golden standard for interactive applications like recommendation systems. Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users. Nevertheless, online evaluation methods are costly for a number of reasons, and a clear need remains for reliable offline evaluation procedures. In industry, offline metrics are often used as a first-line evaluation to generate promising candidate models to evaluate online. In academic work, limited access to online systems makes offline metrics the de facto approach to validating novel methods. Two classes of offline metrics exist: proxy-based methods, and counterfactual methods. The first class is often poorly correlated with the online metrics we care about, and the latter class only provides theoretical guarantees under assumptions that cannot be fulfilled in real-world environments. Here, we make the case that simulation-based comparisons provide ways forward beyond offline metrics, and argue that they are a preferable means of evaluation.

As a 42-year-old, newly single mom, I was a little insecure when I joined Match.com to meet a nice guy. I described myself as a feminist law professor, interested in liberal intellectuals within five years, plus or minus, of my age. The people who contacted me only eroded my confidence, however. I got cryptic messages from much older and more conservative high school grads, pictured on their motorcycles. These suitors and I ostensibly had nothing in common.