While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e.g., demographic parity, equal opportunity) an objective of interest. 'Algorithmic recourse' offers feasible recovery actions to change unwanted outcomes through the modification of attributes. We introduce the notion of ranked group-level recourse fairness, and develop a 'recourse-aware ranking' solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. Our solution suggests interventions that can reorder the ranked list of database records and mitigate group-level unfairness; specifically, disproportionate representation of sub-groups and recourse cost imbalance. This re-ranking identifies the minimum modifications to data points, with these attribute modifications weighted according to their ease of recourse. We then present an efficient block-based extension that enables re-ranking at any granularity (e.g., multiple brackets of bank loan interest rates, multiple pages of search engine results). Evaluation on real datasets shows that, while existing methods may even exacerbate recourse unfairness, our solution -- RAGUEL -- significantly improves recourse-aware fairness. RAGUEL outperforms alternatives at improving recourse fairness, through a combined process of counterfactual generation and re-ranking, whilst remaining efficient for large-scale datasets.

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.

The retailer has also discounted the Echo Show 10. After a 20 percent price drop, the device is $200, down from $250. Both the Charcoal and Glacier White colors are included in the company's latest promotion. We saw Amazon discount the Echo Show 10 to $180 during Prime Day in July, making this the best price we've seen since then. Alongside the Echo Show 15, The Echo Show 10 is one of the more unusual products in Amazon's smart display lineup. Engadget awarded the device a score of 83 in 2021.

At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively. To address these issues, in this paper, we propose a Time-aware Self-Attention with Neural Collaborative Reasoning (TiSANCR) based recommendation model, which integrates temporal patterns and self-attention mechanism into reasoning-based recommendation. Specially, temporal patterns represented by relative time, provide context and auxiliary information to characterize the user's preference in recommendation, while self-attention is leveraged to distill informative patterns and suppress irrelevances. Therefore, the fusion of self-attentive temporal information provides deeper representation of user's preference. Extensive experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms the state-of-the-art recommendation methods.

Artificial intelligence is all about making intelligent machines that can carry out cognitive activities. Once those machines have access to enough data to recognize patterns and trends, their capacity to think like humans will continue to advance. Moreover, artificial intelligence, data, and analytics play a significant role in digital marketing. As a result, any online endeavor must be able to extract the proper insights from data to succeed. Therefore, it makes sense to assume that AI will be essential to digital marketing. This is especially true given the enormous growth in data and its sources that digital marketers need to learn more about.

Today, artificial intelligence (AI) has become an irreplaceable part of how we shop and do business on the web. It's a key component of the underlying infrastructure that brands and retailers rely on to engage customers, track trends, make better business decisions, and provide the most optimal, personalized customer experiences possible. Here are the top nine trends in eCommerce AI that you can expect to see in 2022 and 2023. AI voice assistants like Amazon's Alexa, Apple's Siri, and Google Assistant have become household names used by millions of people worldwide. In fact, 27% of shoppers took advantage of voice assistants to make online purchases in 2020, accounting for $40 billion of revenue in the U.S. and the UK alone.

At the I/O 2022 conference this past May, Google CEO Sundar Pichai announced that the company would, in the coming months, gradually avail its experimental LaMDA 2 conversational AI model to select beta users. On Thursday, researchers at Google's AI division announced that interested users can register to explore the model as access increasingly becomes available. Regular readers will recognize LaMDA as the supposedly sentient natural language processing (NLP) model that a Google researcher got himself fired over. NLPs are a class of AI model designed to parse human speech into actionable commands and are behind the functionality of digital assistants and chatbots like Siri or Alexa, as well as do the heavy lifting for realtime translation and subtitle apps. Basically, whenever you're talking to a computer, it's using NLP tech to listen.

A phone number is a personal identifier that people use to contact someone. While it may seem okay to hand out your number to everyone you meet, it's not always the best idea. If you're not careful, you could inadvertently give out your number to a scammer or someone with malicious intent. And that's where a secret phone number comes in handy. These numbers can be used for a variety of purposes, such as temporary business numbers, disposable numbers for online dating, or one-time use when signing up for new services.

Are you wondering how to text on tinder? If you're interested in the latest and hottest method for meeting women, then you'll want to keep reading. The rise of this dating app has been amazing. It's so much easier now, since there are so many more options. That said, learning how to text on tinder is critical if you want to make a good impression with women.

What is personalized marketing, how does it boost your conversion rate, and how can machine learning help you tailor content to a wider audience?