scholarly journals Recommender systems and their ethical challenges

AI & Society ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 957-967 ◽  
Author(s):  
Silvia Milano ◽  
Mariarosaria Taddeo ◽  
Luciano Floridi
Keyword(s):  
Literature Review ◽  
Recommender Systems ◽  
Recommender System ◽  
Ethical Challenges ◽  
Systematic Analysis ◽  
Areas Of Concern

Abstract This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

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.

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.

scholarly journals Framework for Hybrid Book Recommender System based on Opinion Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 914-919
Keyword(s):  
Literature Review ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Extensive Literature ◽  
Application Area ◽  
Acquisition Process ◽  
Automatic Filtering ◽  
Available Information ◽  
Evaluation Techniques

Recommender system plays an important role in automatic filtering out the important and personalized information for the intended user from a large amount of available information on internet. Recommender systems for books provide personalized recommendations to the readers for reading and to the librarians for book acquisition process. The objective of this research paper is four folds. Firstly, it conducts an extensive literature review pertaining to book recommender systems, secondly it specifies the popular recommendation techniques being used in specific application area of books, thirdly the paper reflects on the methodology followed and evaluation techniques being used based on the techniques discussed. Lastly, the paper proposes a framework for a book recommender system using best-suited recommendation techniques.

scholarly journals Social Media Use Among Young People in China: A Systematic Literature Review

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110164
Author(s):  
Lian Tang ◽  
Siti Zobidah Omar ◽  
Jusang Bolong ◽  
Julia Wirza Mohd Zawawi
Keyword(s):  
Social Media ◽  
Young People ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Media Use ◽  
Academic Research ◽  
Future Research ◽  
Research Topics ◽  
Social Media Use ◽  
Systematic Analysis

The widespread use of social media has promoted extensive academic research on this channel. The present study conducts a systematic analysis of extant research on social media use among young people in China. This systematic literature review aims to identify and bridge gaps in topics, theories, variables, and conceptual frameworks in studies of social media usage among young people in China. The study aims to develop a cause–effect framework that shows the causal relationships among research structures. The PRISMA method is used to review 20 articles drawn from the Scopus and Google Scholar databases. From the analysis, 10 major research topics, eight theories or models, and a complete framework of causal relations emerge. It is recommended that future research on social media should include a greater diversity of types of social media, investigate a wider range of research topics, and adopt different theories or models. Researchers should also implement a more complete and detailed systematic method for reviewing literature on social media research in China.

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.

scholarly journals Role of bevacizumab for treatment-refractory meningiomas: A systematic analysis and literature review

2018 ◽  
Vol 9 (1) ◽  
pp. 133 ◽  
Author(s):  
RicardoJ Komotar ◽  
AaronJ Franke ◽  
WilliamPaul Skelton IV ◽  
LindseyE Woody ◽  
Amade Bregy ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Analysis ◽  
Treatment Refractory

scholarly journals Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

2020 ◽  
Vol 21 (4) ◽  
pp. 255-291
Author(s):  
Asra Khalid ◽  
Karsten Lundqvist ◽  
Anne Yates
Keyword(s):  
Quantitative Analysis ◽  
Literature Review ◽  
Student Performance ◽  
Recommender Systems ◽  
Online Courses ◽  
Systems Design ◽  
Literature Survey ◽  
Massive Open Online Courses ◽  
Future Research ◽  
Massive Open Online

In recent years, massive open online courses (MOOCs) have gained popularity with learners and providers, and thus MOOC providers have started to further enhance the use of MOOCs through recommender systems. This paper is a systematic literature review on the use of recommender systems for MOOCs, examining works published between January 1, 2012 and July 12, 2019 and, to the best of our knowledge, it is the first of its kind. We used Google Scholar, five academic databases (IEEE, ACM, Springer, ScienceDirect, and ERIC) and a reference chaining technique for this research. Through quantitative analysis, we identified the types and trends of research carried out in this field. The research falls into three major categories: (a) the need for recommender systems, (b) proposed recommender systems, and (c) implemented recommender systems. From the literature, we found that research has been conducted in seven areas of MOOCs: courses, threads, peers, learning elements, MOOC provider/teacher recommender, student performance recommender, and others. To date, the research has mostly focused on the implementation of recommender systems, particularly course recommender systems. Areas for future research and implementation include design of practical and scalable online recommender systems, design of a recommender system for MOOC provider and teacher, and usefulness of recommender systems.  

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