Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

Amazon announced its "Day 1" hardware program last year as a way to build unusual hardware products in limited quantities, get feedback from users and eventually make them more widely available. In fact, Amazon's Echo Frames are the first bit of hardware to "graduate" from Day 1 to general availability. Now, the company is expanding Day 1 with a new program called "Built It." Like Day 1, Built It features some unconventional devices, but it's directly taking consumer interest into account when deciding whether to sell the products at all in a way that's similar to what Kickstarter and Indiegogo have been doing. The first three Built It products -- a cuckoo clock, smart nutrition scale and sticky note printer -- were announced today. You can order them now, but Amazon will only make them if they hit a sales goal by March 19th.

Amazon is making it a little easier to get started with a smart home. The internet giant is selling a bundle that pairs the latest-generation Echo with two Philips Hue white light bulbs for $80 -- a full $50 below the official asking price. And when the Echo includes its own Zigbee hub, you won't need to buy any additional bridges to get started. It's not the absolute lowest price we've seen for this combo (it has dipped as low as $70), but we haven't seen a discount this sizeable for months. The Echo may look like a light-up snow globe, but that's about the only significant quirk.

Doesn't work with existing chimes Everything you need to install the Ring Video Doorbell Wired, including mounting hardware, is included with your purchase. Here are the Ring Video Doorbell Wired's specs: It also offers two-way talk with noise cancellation, night vision, and Amazon Alexa integration. The doorbell detects motion and sends smart push notifications to your phone when someone is at the front door. Within the Ring app, you can view a live stream of your doorbell camera, create custom motion zones, and finetune your privacy settings. Installation takes about 15 minutes, and existing doorbell wiring is necessary.

The knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and maintenance processes. We discuss a couple of scenarios for the application of recommender systems in knowledge engineering and report the results of empirical studies which show the importance of user-centered configuration knowledge organization.

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.