Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective.

A Music Recommendation System based on Emotion, Age, and Ethnicity is developed in this study, using FER-2013 and ``Age, Gender, and Ethnicity (Face Data) CSV'' datasets. The CNN architecture, which is extensively used for this kind of purpose has been applied to the training of the models. After adding several appropriate layers to the training end of the project, in total, 3 separate models are trained in the Deep Learning side of the project: Emotion, Ethnicity, and Age. After the training step of these models, they are used as classifiers on the web application side. The snapshot of the user taken through the interface is sent to the models to predict their mood, age, and ethnic origin. According to these classifiers, various kinds of playlists pulled from Spotify API are proposed to the user in order to establish a functional and user-friendly atmosphere for the music selection. Afterward, the user can choose the playlist they want and listen to it by following the given link.

Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized deep learning-based recommender system for yourself? If Yes! Then this is the course you are looking for. You might have searched for many relevant courses, but this course is different!

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n=24) and one experimental study comparing voice and body morphologies (n=36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed.

Deutsche Bank is looking to deploy artificial intelligence (AI) acceleration technology from Nvidia to power financial services applications. The bank hopes AI will improve its efforts to serve customers worldwide and enable it to build new data-driven products and services, increase efficiency and recruit tech talent. Using Nvidia AI Enterprise software, Deutsche Bank said its AI developers, data scientists and IT professionals would be able to build and run AI workflows in hosted on-premise datacentres as well as on Google Cloud, which the bank uses as its public cloud provider. The bank plans to use the latest version of Nvidia's enterprise AI tool – AI Enterprise 3.0. This introduces workflows for contact centre intelligent virtual assistants, audio transcription and digital fingerprinting for cyber security.

Amazon's Echo Show 15 is now useful as a tiny TV. The company has released a promised free update that brings the Fire TV interface to the smart display. As on other devices, you can stream from a range of apps (including Netflix, Prime Video and YouTube) with an on-screen carousel that helps you find content and resume shows. This will be familiar to anyone who's used even a basic Fire TV Stick, but the Echo Show's design offers a few interface twists. You can use Alexa to open apps or stream specific content, and use either a paired Alexa Voice Remote or the controls in the mobile Fire TV app to navigate without smudging the screen.

The Echo Show 5 is one of Amazon's most versatile Alexa devices thanks to its 5.5-inch display, it's relatively solid sound quality and it's compact size. The online retailer discounted the smart display to $35 for the holiday shopping season, but it also has a deal on a bundle that would make a good smart-home starter kit. You can pick up the Echo Show 5 with a Blink Mini for a total of $50, which is 58 percent off the normal price of the bundle. Amazon's nightstand-friendly smart display paired with a Blink Mini security camera is 58 percent off right now. Amazon last updated the Echo Show 5 in 2021, giving it a 2-megapixel front camera that will slightly improve the quality of your video chats.