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Practical recommender systems
Online recommender systems help users find movies, jobs, restaurants, even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way : it can make or break your application! \"Practical recommender systems\" explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows.
Book
Recommender system with machine learning and artificial intelligence : practical tools and applications in medical, agricultural and other industries
This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
eBook
Fundamental relationship between bilateral kernel and locally adaptive regression kernel
The relationship between the bilateral kernel function and the recently proposed locally adaptive regression kernel is examined. Despite the difference in implementation, both locally adaptive approaches are designed to prevent averaging across edges while smoothing an image. Their similarity suggests that they can reasonably be linked although both filtering approaches have grown to become well-established theories in their fields. First, the locally adaptive regression kernel is analysed theoretically. Then, the connection between the methods is explored by applying the spectral distance measure to the bilateral kernel. Finally, a direct relation is established between the bilateral kernel and the locally adaptive regression kernel. [PUBLICATION ABSTRACT]
Journal Article
Spatiotemporal 3D motion vector filtering method for robust visual odometry
Most of the previous visual odometry methods cannot deal with a large independently moving object that takes up over 50% of the image area. To overcome this problem, the spatiotemporal filter is incorporated into the RANSAC method to filter out false match that occurs by a large independently moving object. This spatiotemporal filter uses the current and previous motion vector's length and direction. Experimental results demonstrate that the proposed method effectively rejects the motion vectors generated from large independently moving objects and improves the visual odometry accuracy.
Journal Article
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.
Journal Article
Doublet identification in single-cell sequencing data using scDblFinder version 1; peer review: 1 approved, 1 approved with reservations
Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed
scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets,
scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.
Journal Article
A Comprehensive Study of Recommender Systems for the Internet of Things
Internet of Things (IoT) is an advancing technology that is a network of many smart devices connected together to provide services to various application domains such as smart offices, health monitoring, agriculture, etc. IoT-based recommendation technologies are becoming one of the key requirements that will recommend future IoT solutions. A review of existing recommendation technologies in the vibrant field of IoT is discussed in this paper. The main aim of this paper is to present a comprehensive analysis of existing literature on recommendation approaches. Several issues of applying recommendation systems to IoT are also discussed. Nearly 1000 research papers have been considered for analyses which are published by ACM, Springer, IEEE and Science Direct from 2011 to 2017. Finally, the recent research trends are spotted for future researchers intended to work in the recommendation-based IoT domain. Moreover, this paper also envisages the future of the Recommender System (RS) that opens up the newest research directions for young researchers.
Journal Article
Low-complexity near-optimal signal detection for uplink large-scale MIMO systems
The minimum mean square error (MMSE) signal detection algorithm is near-optimal for uplink multi-user large-scale multiple-input–multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from 𝒪(K3) to 𝒪(K2), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.
Journal Article