First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.

This timely volume combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools. The authors give special attention to key topics such as data filtering and cleaning techniques for recommendations, novelty and diversity, privacy issues, security threats and their mitigation, trust, cold start, sparsity, scalability, application domains, and recommender system evaluations.

The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.

Cloud computing has been a game changer for internet-based applications such as content delivery networks, social networking and multi-tier enterprise applications. However, the requirements for low-latency data access, security, bandwidth, mobility, and cost have challenged centralized data center-based cloud computing models, which is driving the need for the novel computing paradigms of edge and fog computing. The internet of things (IoT) focuses on discovery, aggregation, management, and acting on data originating from internet-connected devices via programmable sensors, actuators, mobile phones, surveillance cameras, routers, gateways and switches. But the aggregation of this data is expensive and can be time consuming.

The needs of future digital data and computer systems are expected to be two to three orders of magnitude larger than for today's systems, to take account of unprecedented amounts of heterogeneous hardware, lines of source code, numbers of users, and volumes of data. Ultrascale computing systems (UCS) are a solution. Envisioned as large-scale complex systems joining parallel and distributed computing systems, which can be located at multiple sites and cooperate to provide the required resources and performance to the users, these technologies will extend individual systems to provide the resources that are very much needed.