Artificial intelligence has significantly changed the way we interact with technology. Although some may not realize it, artificial intelligence has become part of everyone's daily lives. An overview of how artificial intelligence can help in making decisions can be found here. Amazon Echo and Google Homeowners know how convenient these AI-powered devices are, especially given their ability and accuracy. During voice searches, AI can deliver results and enhance customer experience by seamlessly processing voice commands. These statistics show the extent to which artificial intelligence has grown.

Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendation quality can vary by demographic groups. Marginalized groups or groups that are under-represented in the training data may receive less relevant recommendations from these algorithms compared to others. In a recent study, Ekstrand et al. [15] investigate how recommender performance varies according to popularity and demographics, and find statistically significant differences in recommendation utility between binary genders on two datasets, and significant effects based on age on one dataset. Here we reproduce those results and extend them with additional analyses. We find statistically significant differences in recommender performance by both age and gender. We observe that recommendation utility steadily degrades for older users, and is lower for women than men. We also find that the utility is higher for users from countries with more representation in the dataset. In addition, we find that total usage and the popularity of consumed content are strong predictors of recommender performance and also vary significantly across demographic groups.

Today, we're excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users' interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user's interests, we change their experiences and the items they see. Visitors are often recommended items that users with similar histories have interacted with. These recommendations are called similar items, and they help users discover items relevant to what they're watching or purchasing. By taking into account the item a user is engaged with, we can improve engagement and conversion.

Tinder wants to help you find a date for the next wedding you plan to attend. The dating app now includes a " Plus One" feature that allows you to indicate whether you're looking for a wedding date or open to be that person for someone else. You'll find the experience inside the Explore tab. That's the same section of the app where you can pay for a Lyft ride for your date. In a way, the introduction of Plus One is a response to something Tinder users already come to the app to find.

Get ready to discover a world beyond Bitcoin with iBG. For the common man, it was just Bitcoin. This was and probably still is the original cryptocurrency. It is also the most valuable cryptocurrency. But things are going to change, or should we say that they have already? The whole asset class of virtual currencies is in for a flip.

If you missed the chance to grab the new Echo Show 8 during Amazon's Prime Day event in July, you may want to check the smart display's listing on Amazon. That's only $5 more than what it was listed for during Prime Day, and it's certainly not a bad deal for a relatively new device that was only released in June. We gave the Echo Show 8 a score of 87 in our review. Between this device and its smaller 5-inch sibling, it received more upgrades from the previous generation, including a faster octa-core processor. It also has a 13-megapixel wide-angle camera that's a huge improvement over the previous version's one-megapixel sensor.

Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research in this direction, we propose IGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment. Understanding the complexity of the challenge, we split it into sub-tasks to make it feasible for participants. This research challenge is naturally related, but not limited, to two fields of study that are highly relevant to the NeurIPS community: Natural Language Understanding and Generation (NLU/G) and Reinforcement Learning (RL). Therefore, the suggested challenge can bring two communities together to approach one of the important challenges in AI. Another important aspect of the challenge is the dedication to perform a human-in-the-loop evaluation as a final evaluation for the agents developed by contestants.

Artificial Intelligence (AI) has recently become the current commercial buzzword. AI offers tremendous promise for analyzing data and extracting relevant insights that may be used to make critical strategic business choices. Businesses all around the globe are searching for methods to make use of the advantages of sophisticated technology to help them expand. Artificial Intelligence has emerged as a breakthrough technical advancement that has altered the whole commercial environment, thanks to its extraordinary capacity to aid organizations in making critical decisions. AI and its integration with various applications are assisting businesses in generating massive profits.

The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimation of user preferences based on sparse and noisy historical data. Conversational Recommendation System (CRS) uses the interactive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems. However, due to the lack of contextual information modeling, the existing CRS models are unable to deal with the exploitation and exploration (E&E) problem well, resulting in the heavy burden on users. To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling (KGenSam). KGenSam integrates the dynamic graph of user interaction data with the external knowledge into one heterogeneous Knowledge Graph (KG) as the contextual information environment. Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem. Experimental results on two real-world datasets demonstrate the superiority of KGenSam with significant improvements over state-of-the-art methods.

POI recommendation is a key task in tourism information systems. However, in contrast to conventional point of interest (POI) recommender systems, the available data is extremely sparse; most tourist visit a few sightseeing spots once and most of these spots have no check-in data from new tourists. Most conventional systems rank sightseeing spots based on their popularity, reputations, and category-based similarities with users' preferences. They do not clarify what users can experience in these spots, which makes it difficult to meet diverse tourism needs. To this end, in this work, we propose a mechanism to recommend POIs to tourists. Our mechanism include two components: one is a probabilistic model that reveals the user behaviors in tourism; the other is a pseudo rating mechanism to handle the cold-start issue in POIs recommendations. We carried out extensive experiments with two datasets collected from Flickr. The experimental results demonstrate that our methods are superior to the state-of-the-art methods in both the recommendation performances (precision, recall and F-measure) and fairness. The experimental results also validate the robustness of the proposed methods, i.e., our methods can handle well the issue of data sparsity.