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.

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.

scholarly journals An Innovative Recommender System for E-Commerce Websites using Natural Language Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4085-4089
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Language Processing ◽  
Recommender System ◽  
Opinion Mining ◽  
The Internet

A Recommender System has become the go-to application for the internet generation these days. Mono-variate, bi-variate and multi-variate Recommender Systems are available to consumers of various products and services for the last 10 years or so only. In this paper, opinion mining dependent sentiment analysis using NLP tools will be used to recommend products to their purchasers on e-commerce websites. The application can be developed on the Python platform can be commercially used and will be precisely used to people who have to spend money without traditionally touching or feeling the item

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

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 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.

scholarly journals Nesting in the lizard Phyllopezus pollicaris (Squamata: Phyllodactylidae) and a phylogenetic perspective on communal nesting in the family

2017 ◽  
Vol 16 (2) ◽  
pp. 255
Author(s):  
Fabricius M. C. B. Domingos ◽  
Ísis C. Arantes ◽  
Renan J. Bosque ◽  
Marcella G. Santos
Keyword(s):  
Literature Review ◽  
Phylogenetic Tree ◽  
Reproductive Investment ◽  
Extensive Literature ◽  
Communal Nesting ◽  
The Family ◽  
Reptile Species ◽  
Phylogenetic Perspective ◽  
Reproduction Pattern ◽  
Available Information

Communal nesting occurs in many reptile species. The hypotheses that explain the evolution of such behaviours are still controversial, but will be better understood as more communal nesting records are described in the literature. We report the findings of two communal nests of Phyllopezus pollicaris, including the largest known nest for the species. Our study is the first to provide a communal nesting record for squamate species in a Cerrado core area. We provide nest photographs and detailed neonate measurements and weight. Nests were found during the dry season, in contrast with the reproduction pattern previously described for the species in the Cerrado periphery. We also conducted an extensive literature review seeking all available information on communal nesting information in Phyllodactylidae, and present this information in the context of a phylogenetic tree of phyllodactylid genera. We suggest that studying the correlation between communal nesting evolution and reproductive investment will become a fertile field as more information on lizard nesting becomes available in the literature.

A Deployed People-to-People Recommender System in Online Dating

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 5-18 ◽  
Author(s):  
Wayne Wobcke ◽  
Alfred Krzywicki ◽  
Yang Sok Kim ◽  
Xiongcai Cai ◽  
Michael Bain ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Online Dating ◽  
Hybrid Algorithms ◽  
Application Area ◽  
Limited Information ◽  
Matching Market

Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.

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.

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