Recommender Systems in Healthcare

2018 ◽  
pp. 323-346 ◽  
Author(s):  
Adekunle Oluseyi Afolabi ◽  
Pekka Toivanen
Keyword(s):  
Real Time ◽  
Recommender Systems ◽  
Recommender System ◽  
Evaluation Criteria ◽  
Upward Trend ◽  
Empirical Results ◽  
Care Giving

In this chapter the appropriateness of any recommender system in healthcare, which lies in its ability to provide capabilities for meeting the challenges of modern care giving, is examined. The impacts of over two decades of research in and implementation of recommender systems in healthcare are extensively examined in two consecutive periods: first to examine empirical results and practical implementations while the second focuses on validating the earlier findings and justifying the propositions made. Although the result indicates an optimistic progress and upward trend in both the research and implementation, there are compelling reasons to invest more efforts at harmonizing evaluation criteria and metrics. In addition, in order to appropriately, adequately, and effectively meet the challenges of modern care, the rapidly evolving trends, and changing technologies, a novel solution with potential for these capabilities is proposed: a solution to provide real-time recommendations and make them available for sharing among stakeholders in real time.

Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems

2018 ◽  
pp. 2206-2226
Author(s):  
Adekunle Oluseyi Afolabi ◽  
Pekka Toivanen ◽  
Keijo Haataja ◽  
Juha Mykkänen
Keyword(s):  
Literature Review ◽  
Home Care ◽  
Recommender Systems ◽  
Systematic Literature Review ◽  
Recommender System ◽  
Empirical Results ◽  
The Core ◽  
Extensive Search ◽  
Speed And Accuracy ◽  
Medical And Healthcare

This systematic literature review is aimed at examining empirical results and practical implementations of healthcare recommender systems. While fundamentally many of the development of recommender systems in medical and healthcare are based on theory and logic, the performance is always measured in terms of empirical results and practical implementations from evaluation of such systems. Besides, the ultimate judgment of the effectiveness of the methods and algorithms used is often based on the empirical results of recommender systems. Robustness, efficiency, speed, and accuracy are also best determined by empirical results. Extensive search was carried out in some major databases. Literature were grouped into three categories namely core, related, and relevant. The core papers were subjected to further analysis. The result shows that most work reviewed were partially evaluated and have a promising future. Moreover, a yet-to-be explored novel proposal for integration of a recommender system into smart home care is presented.

Distributed Recommender Systems

2001 ◽  
pp. 372-393 ◽  
Author(s):  
Badrul M. Sarwar ◽  
Joseph A. Konstan ◽  
John T. Riedl
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Recommender Systems ◽  
Recommender System ◽  
Design Issues ◽  
Internet Commerce ◽  
Basic Principles ◽  
Analysis Techniques

In this chapter, we introduce the concepts of recommender systems as a very successful Internet commerce tool. Then, we describe the basic principles of recommender systems and carefully analyzes how these systems relate to other prevailing data-analysis techniques and how they are more suitable for providing real-time personalized recommendations for customers of Internet commerce. The following section depicts the importance of recommender systems and their strategies for improving sales. We then analyze the nature and necessity of recommender systems in future commerce applications and establish the need for distributing such services to make them widely available. Later we present a detailed taxonomy of distributed recommender system applications and three different implementation frameworks for providing distributed recommender system services for Internet commerce, we analyze some of the design issues as well.

Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems

2015 ◽  
Vol 10 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Adekunle Oluseyi Afolabi ◽  
Pekka Toivanen ◽  
Keijo Haataja ◽  
Juha Mykkänen
Keyword(s):  
Literature Review ◽  
Recommender Systems ◽  
Systematic Literature Review ◽  
Recommender System ◽  
Smart Home ◽  
Empirical Results ◽  
The Core ◽  
Extensive Search ◽  
Speed And Accuracy ◽  
Medical And Healthcare

This systematic literature review is aimed at examining empirical results and practical implementations of healthcare recommender systems. While fundamentally many of the development of recommender systems in medical and healthcare are based on theory and logic, the performance is always measured in terms of empirical results and practical implementations from evaluation of such systems. Besides, the ultimate judgment of the effectiveness of the methods and algorithms used is often based on the empirical results of recommender systems. Robustness, efficiency, speed, and accuracy are also best determined by empirical results. Extensive search was carried out in some major databases. Literature were grouped into three categories namely core, related, and relevant. The core papers were subjected to further analysis. The result shows that most work reviewed were partially evaluated and have a promising future. Moreover, a yet-to-be explored novel proposal for integration of a recommender system into smart home care is presented.

Evaluation Criteria for Real-Time Specification Languages

1992 ◽  
Author(s):  
Paul C. Clements ◽  
Carolyn E. Gasarch ◽  
Ralph D. Jeffords
Keyword(s):  
Real Time ◽  
Evaluation Criteria ◽  
Specification Languages

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.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

Sign in / Sign up

Export Citation Format

Share Document