Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper, we present Deep Retrieval (DR), an end-to-end learnable structure model for large-scale recommendations. DR encodes all candidates into a discrete latent space. Those latent codes for the candidates are model parameters and to be learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates. Empirically, we showed that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline.

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

Artificial Intelligence seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These kind of problems are present in all fields, the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

This is what Elon Musk and similar people are fearful of for controlling the world. This brings us to the fact that we need more computing resources to handle the corpus of data which unfortunately is limited. Therefore, we need to work through a rule-based programming – hence the shift away from AI towards ML.

In mathematics, a type of variation called direct variation describes'a simple relationship between two variables -- such that we observe an increase of one variable when the other increases, or a decrease of the same when the other reduces'. This law works in business as well, wherein the population of the workforce determines the productivity of the company. However, recent development in artificial intelligence defies this law. In 2020, a business could have only a team of essential workers with a couple of well-utilized computers and do more than a larger company which has the help of its thousands of workers but without digital intervention or its innovative implementation whatsoever. The usefulness of computer technology in business has been paced recently in such a way that most businesses will not need to hire more workers in the future.

In this section, we will talk about Artificial Intelligence, its history, applications, the different types of AI, and the programming languages that are used for AI. Note that I will not be talking about how to code AI but mainly focus on the various languages which support AI. No, don't close this tab!!! Ok fine, I'll start doing my job of explaining properly. "The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." In simple words, AI is the science of making machines that can think. It's a technique of getting machines to work and behave like humans which accomplishes this task by creating machines and robots.

Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recommender tasks and have made remarkable progress. At its core is to explicitly capture high-order connectivities between the nodes in user-item bipartite graph. However, we theoretically and empirically find an inherent drawback existed in these GCN-based recommendation methods, where GCN is directly applied to aggregate neighboring nodes will introduce noise and information redundancy. Consequently, the these models' capability of capturing high-order connectivities among different nodes is limited, leading to suboptimal performance of the recommender tasks. The main reason is that the the nonlinear network layer inside GCN structure is not suitable for extracting non-sematic features(such as one-hot ID feature) in the collaborative filtering scenarios. In this work, we develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF), where the construction of the embeddings of users (items) are delicately redesigned from several aspects during the aggregation on the graph. Compared to the state-of-the-art GCN-based recommendation, RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive. We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models. We release our code at https://github.com/hfutmars/RGCF.

Dating in 2020 is a roller coaster, from endless swiping to video chat dates, the worry that your quarantine-boo might be fake is all too real. "I've been on Tinder on-and-off for the past three years, but have been back on since March when the pandemic started. I have been seeing more bots than usual," said Carlos Zavala, 25, of his dating experience. Online dating in the U.S. has become the most popular way couples connect, a Stanford study published in 2019 found. That finding is being put to the test with the outbreak of the coronavirus in the U.S. since mid-March.

Dating in 2020 is a roller coaster, from endless swiping to video chat dates, the worry that your quarantine-boo might be fake is all too real. "I've been on Tinder on-and-off for the past three years, but have been back on since March when the pandemic started. I have been seeing more bots than usual," said Carlos Zavala, 25, of his dating experience. Online dating in the U.S. has become the most popular way couples connect, a Stanford study published in 2019 found. That finding is being put to the test with the outbreak of the coronavirus in the U.S. since mid-March.