Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of the training data and cannot model the casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various graph counterfactual learning approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on research problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a ``one-stop-shop'' for building a unified understanding of graph counterfactual learning categories and current resources. We also maintain a repository for papers and resources and will keep updating the repository https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning.

Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context.

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (Chat-GPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.

The Alexa assistant is always ready to answer our questions, the need for fast and instant service, always staying online, connected and alert. In the age of smartphones, Amazon has designed a device without a screen, with which users can interact. The concept would seem unusual, but it has nevertheless been incredibly successful. Alexa's interactive skills involve the user in a unique way, a single platform where multiple brands can get in touch with the customer. The level of convenience and practicality in using the Alexa voice assistant suggests that in the future there may be improvements in performance and in the user experience.

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. For all my talk of various kinks and thwarted online assignations, I do have some good news: I've found a lover. His name is Seth, and I met him after he liked my profile back in December. He's 31 and works in a skateboard shop in the suburbs--a bit of a haul from me--but he's got a dependable old Subaru and is willing to drive to my neck of the woods to have sex. Which isn't to say that he just comes over and we fuck.

Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.

Artificial Intelligence (AI) is a rapidly growing field that has already begun to transform the world we live in. From healthcare to finance, manufacturing to transportation, AI is making its presence felt in almost every aspect of our lives. However, many people are still unsure of what AI is, how it works, and what its potential impact on society may be. In this blog, we will provide a comprehensive guide to understanding AI for beginners. AI is the simulation of human intelligence in machines that are programmed to learn from data and perform tasks that typically require human cognition, such as visual perception, speech recognition, decision-making, and language translation.

Hurricane Ian made its way across Florida in late September 2022, causing tens of billions in estimated insurance losses due to wind and flood damage. Now, half a year later after the disaster, homeowners are still picking up the pieces and rebuilding with the payouts that have been slowly coming out from insurance policies. However, many have had the unexpected shock to learn that flooding was not a part of their homeowners insurance. Here we explain natural disasters, AI and insurance risk assessment. This event and many like it are stark reminders to both individuals and businesses that checking in with their insurance company to review insurance policies is something that needs to happen regularly, not because something may have gone unnoticed, but because things change.

CNBC reports that Google is reorganizing the management hierarchy within its virtual assistant division--Assistant--to concentrate on Bard. Last week, Google introduced Bard, its ChatGPT competitor, to the public as an experimental project. Previous reports indicate Google has been reallocating team members from various departments to concentrate on Bard as part of a "code red" effort. CNBC's report suggests that the effort is still ongoing. Google hasn't responded to requests for comment on CNBC's report.

Nearly a year after he first floated the idea of making Twitter's recommendation algorithm public, the company has posted the source code for its recommendation algorithm on GitHub. In a Twitter Space discussing the move, Elon Musk said he hoped users would be able to find potential "issues" in the code and help make it better. "Our initial release of the so-called algorithm is going to be quite embarrassing and people are gonna find a lot of mistakes but we're going to fix them very quickly," Musk said. Notably, the code released Friday only deals with how tweets are shown in Twitter's "For You" feed. The company didn't release the underlying code for its search algorithm or how content is displayed on other parts of Twitter, though Musk said the company would "for sure" open-source the search algorithm as well.