The purpose of the Session-Based Recommendation System is to predict the user's next click according to the previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose a heterogeneous graph neural network-based session recommendation method, named SR-HetGNN, which can learn session embeddings by heterogeneous graph neural network (HetGNN), and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, to consider the influence of users' long and short-term preferences, local and global session embeddings are combined with the attentional network to obtain the final session embedding. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.

Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e.g., clicks, add-to-cart, purchases, etc. To exploit these user-item interactions, there are increasing efforts on considering the user-item interactions as a user-item bipartite graph and then performing information propagation in the graph via Graph Neural Networks (GNNs). Given the power of GNNs in graph representation learning, these GNN-based recommendation methods have remarkably boosted the recommendation performance. Despite their success, most existing GNN-based recommender systems overlook the existence of interactions caused by unreliable behaviors (e.g., random/bait clicks) and uniformly treat all the interactions, which can lead to sub-optimal and unstable performance. In this paper, we investigate the drawbacks (e.g., non-adaptive propagation and non-robustness) of existing GNN-based recommendation methods. To address these drawbacks, we propose the Graph Trend Networks for recommendations (GTN) with principled designs that can capture the adaptive reliability of the interactions. Comprehensive experiments and ablation studies are presented to verify and understand the effectiveness of the proposed framework. Our implementation and datasets can be released after publication.

Now that many kids are about to go back to school, Google thinks it can offer a helping hand -- including after class. It's introducing Assistant and search features to help parents coordinate in morning and kids to learn more (or at least, stay entertained). To start, Family Bell is coming to mobile devices. Accordingly, it can soon start a checklist on a Nest Hub to remind kids to make the bed and brush their teeth before they fly out the door. Kids will also have more ways to improve their education at home.

Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. As a science manager for the ML Solutions Lab team, you will lead a team of customer-facing scientists and architects to design and deliver advanced ML solutions to solve diverse real-world problems for customers across all industries. You will interact with customers, translate their business problems into ML problems, and lead your team in applying classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.

What is the most challenging aspect of marketing? Identifying your potential customer, predicting their interest, engaging, and nurturing them for an ultimate buyout is not that easy. This is where Artificial Intelligence can help you analyze the customer pool, identify and segregate lead. Further, you can even nurture them using AI-based algorithms, which helps in delivering high value. Take an example of the famous inbound marketing giant Hubspot.

While the Apple TV 4K tends to get all of the attention, the standard Apple TV remains a solid option if you want a set-top box that plays nicely with the rest of your iPhone, iPad and other devices. It'll normally set you back $150, but Amazon is having a sale right now that knocks $20 off the Apple TV HD, bringing it down to $130. That's the best price we've seen since the streamer came out a few months ago, plus it includes the updated Siri remote -- arguably our favorite thing about the Apple TV experience as of late. Despite recent updates, the Apple TV looks familiar. It runs on Apple's A8 processor, comes with 32GB of onboard storage and the back edge houses a power port, one USB-C port, one HDMI connector and an Ethernet port.

Surprise (stands for Simple Python RecommendatIon System Engine) is a Python library for building and analyzing recommender systems that deal with explicit rating data. It provides various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD, NMF), and many others. Also, various similarity measures (cosine, MSD, Pearson…) are built-in. In both, I will use collaborative filtering techniques and content-based techniques to filter items, and no worries I will explain the differences between them. I use here the MovieLens dataset.

Conversational AI amalgamates traditional software, such as chatbots or some form (voice or text) of interactive virtual assistants, with large volumes of data and machine learning algorithms to mimic human interactions. This imitation of human interactions is made possible by its underlying technologies -- machine learning, more specifically, Natural Language Processing (NLP). Conversational AI can recognize speech input and text input and translate the same across various languages to provide customer support using either a typed or spoken interface. A voice assistant or a chatbot empowered by conversational AI is not only a more intuitive software for the end user but is also capable of comprehensively understanding the nuances of a human query. Hence, conversational AI, in a sense, enables effective communication and interaction between computers and humans.

User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.

Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The pro- posed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.