System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.

Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus of the KnowledgeCheckR environment is to take into account forgetting processes which immediately start after a learning unit has been completed. In this context, techniques are needed that are able to predict which learning units are the most relevant ones to be repeated in future learning sessions. In this paper, we provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering. In order to show the applicability of the presented techniques, we provide an overview of the results of empirical studies that have been conducted in real-world scenarios.

A significant amount of research has been devoted to automatic personalization in digital applications, especially in Internet applications Computer games represent an ideal research domain for the next [8]. As the content of the Internet services grows, personalized generation of personalized digital applications. This paper presents applications such as recommendation systems help to mitigate information a player-centered framework of AI for game personalization, complementary overload and decision fatigue [8]. This body of work to the commonly used system-centered approaches.

As Valentine's Day approaches, couples across the country are preparing for this long-standing tradition--and there's a very good chance they met through online dating. But while dating apps can help people find a partner (or just a fun date), they can also subject users to incredible hate and harassment. Despite the fact that dating apps have accrued significant reach and influence, these companies provide very little transparency around how they keep users safe and how they moderate content. Much of the conversation around online platform accountability focuses on companies like Facebook and Google. But dating apps face many of the same issues.

A recommendation engine is a class of machine learning algorithm that suggests products, services, information to users based on analysis of data. Robust recommendation systems are the key differentiator in the operations of big companies like Netflix, Amazon, and Byte Dance (TikTok parent) etc. Alok Menthe, Data Scientist at Ericsson, gave an informative talk on building'Custom recommendation engines for real-world problems' at the Machine Learning Developers Summit (MLDS) 2021. "Whenever a niche business problem comes in, it has complicated intertwined ways of working. Standard ML techniques may be inadequate and might not serve the customer's purpose. That is where the need for a custom-made engine comes in. We were also faced with such a problem with our service network unit at Ericsson," he said.

New York – Dating apps offer a snapshot about a person's life, but in the space of a few weeks, a surprising health issue has emerged as a dealmaker or heartbreaker: Have you had the coronavirus vaccine? Some are bragging they have gotten the shot in order to better their chances, while others are using it to justify what one singleton described as "the most 2021 rejection ever." But can you trust every lonely heart who claims they've been inoculated against COVID-19? Samantha Yammine, a scientist who often talks on Twitter about health issues, says she's received messages about "dudes on dating apps claiming they're'totally safe for close contact' because they have received the vaccine." Of course, most young people using dating apps are not in vaccination priority groups at the front of the line, so some see having gotten the shot as a sort of golden ticket for hooking up.

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.

Online gaming is a multi-billion-dollar industry, which is growing faster than ever before. Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M$^3$Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M$^3$Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M$^3$Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M$^3$Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.

There are many sources that give similar answers to the question, 'What is AI?' By the 1950s, there were many scientists, mathematicians, and philosophers that were looking into the concept of Artificial Intelligence. One such person was Alan Turing, who to this day is considered by many to be the Father of Artificial Intelligence. He formed the idea and mathematical and logical reasoning behind the concept of machine intelligence wherein machines and computers would be able to replicate the behavior of humans and their intelligence. His paper Computing Machinery and Intelligence outlines his logic for the start of artificial intelligence. Fast forward 70 years into the future and we are now in a world where computers are able to converse with humans, albeit with limitations, but this is the progress we see as our world progresses to a more sophisticated AI. "The design and development of computer systems that have the knowledge and skills required to perform the tasks which usually require human intelligence to undertake" –AILab "The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings."

There's been such a rise in intentional dating, with more people looking for something serious because they're reminded of the value of human connection. And, of course, people have so much more free time.