Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

2005 ◽  
Vol 17 (6) ◽  
pp. 734-749 ◽  
Author(s):  
G. Adomavicius ◽  
A. Tuzhilin
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
The State ◽  
Next Generation

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

scholarly journals User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 93 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Preference Elicitation ◽  
Design Guidelines ◽  
User Preferences ◽  
The State ◽  
User Preference ◽  
Large Collection ◽  
Complete Model ◽  
Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

scholarly journals A Survey of Collaborative Filtering Techniques

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoyuan Su ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
State Of The Art ◽  
Predictive Performance ◽  
The State ◽  
Data Sparsity ◽  
Research And Practice ◽  
Model Based ◽  
Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

scholarly journals Multiplexed Immunohistochemistry and Digital Pathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications

2021 ◽  
Vol 11 ◽  
Author(s):  
Yannick Van Herck ◽  
Asier Antoranz ◽  
Madhavi Dipak Andhari ◽  
Giorgia Milli ◽  
Oliver Bechter ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Single Cell ◽  
State Of The Art ◽  
Deep Understanding ◽  
Response Rates ◽  
The State ◽  
Patient Specific ◽  
Specific Cell ◽  
Next Generation ◽  
Single Cell Profiling

The state-of-the-art for melanoma treatment has recently witnessed an enormous revolution, evolving from a chemotherapeutic, “one-drug-for-all” approach, to a tailored molecular- and immunological-based approach with the potential to make personalized therapy a reality. Nevertheless, methods still have to improve a lot before these can reliably characterize all the tumoral features that make each patient unique. While the clinical introduction of next-generation sequencing has made it possible to match mutational profiles to specific targeted therapies, improving response rates to immunotherapy will similarly require a deep understanding of the immune microenvironment and the specific contribution of each component in a patient-specific way. Recent advancements in artificial intelligence and single-cell profiling of resected tumor samples are paving the way for this challenging task. In this review, we provide an overview of the state-of-the-art in artificial intelligence and multiplexed immunohistochemistry in pathology, and how these bear the potential to improve diagnostics and therapy matching in melanoma. A major asset of in-situ single-cell profiling methods is that these preserve the spatial distribution of the cells in the tissue, allowing researchers to not only determine the cellular composition of the tumoral microenvironment, but also study tissue sociology, making inferences about specific cell-cell interactions and visualizing distinctive cellular architectures - all features that have an impact on anti-tumoral response rates. Despite the many advantages, the introduction of these approaches requires the digitization of tissue slides and the development of standardized analysis pipelines which pose substantial challenges that need to be addressed before these can enter clinical routine.

scholarly journals Next-Generation Gene Therapy for Parkinson’s Disease Using Engineered Viral Vectors

2021 ◽  
pp. 1-9
Author(s):  
Tomas Björklund ◽  
Marcus Davidsson
Keyword(s):  
Parkinson’S Disease ◽  
Gene Therapy ◽  
Parkinson's Disease ◽  
Clinical Trials ◽  
Genome Editing ◽  
Viral Vectors ◽  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Suitable Vector

Recent technological and conceptual advances have resulted in a plethora of exciting novel engineered adeno associated viral (AAV) vector variants. They all have unique characteristics and abilities. This review summarizes the development and their potential in treating Parkinson’s disease (PD). Clinical trials in PD have shown over the last decade that AAV is a safe and suitable vector for gene therapy but that it also is a vehicle that can benefit significantly from improvement in specificity and potency. This review provides a concise collection of the state-of-the-art for synthetic capsids and their utility in PD. We also summarize what therapeutical strategies may become feasible with novel engineered vectors, including genome editing and neuronal rejuvenation.

“The Enthusiasm Expressed by Our Industry Friends”

Vulcan ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 100-124
Author(s):  
Adam Givens
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Short Term ◽  
Us Army ◽  
The Face ◽  
Long Term Consequences ◽  
Fait Accompli

Abstract This article analyzes the groundbreaking 1952 plan by US Army leadership to develop a sizeable cargo helicopter program in the face of interservice opposition. It examines the influence that decision had in the next decade on the Army, the helicopter industry, and vtol technology. The Army’s procurement of large helicopters that could transport soldiers and materiel was neither a fait accompli nor based on short-term needs. Rather, archival records reveal that the decision was based on long-range concerns about the postwar health of the helicopter industry, developing the state of the art, and fostering new doctrinal concepts. The procurement had long-term consequences. Helicopters became central to Army war planning, and the ground service’s needs dictated the next generation of helicopter designs. That technology made possible the revolutionary airmobility concept that the Army took into Vietnam and also led to a flourishing commercial helicopter field.

scholarly journals The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 18
Author(s):  
Deepani B. Guruge ◽  
Rajan Kadel ◽  
Sharly J. Halder
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Current Knowledge ◽  
Research Literature ◽  
The State ◽  
Knowledge Based ◽  
Emerging Trends ◽  
Wide Range ◽  
Research Findings ◽  
Research Outcome

In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

scholarly journals Next-Generation Electronics and Sensing Technology

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7958
Author(s):  
Trong-Yen Lee ◽  
Yen-Lin Chen ◽  
Yu-Cheng Fan
Keyword(s):  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Special Issue ◽  
Sensing Systems ◽  
Sensing Technology ◽  
Art Methods

This Special Issue is dedicated to several aspects of next-generation electronics and sensing technology and contains eight papers that focus on advanced sensing devices, sensing systems, and sensing circuits that focus on the state-of-the-art methods for sensing technologies [...]

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