Introduction to big data recommender systems — volume 1

2019 ◽  
pp. 1-7
Author(s):  
Osman Khalid ◽  
Faisal Rehman ◽  
Samee U. Khan ◽  
Albert Y. Zomaya
Keyword(s):  
Big Data ◽  
Recommender Systems

The evolution of recommender systems: From the beginning to the Big Data era

2017 ◽  
pp. 253-283
Author(s):  
Beatrice Paoli ◽  
Monika Laner ◽  
Beat Tödtli ◽  
Jouri Semenov
Keyword(s):  
Big Data ◽  
Recommender Systems

scholarly journals Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

2019 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Will Serrano
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Recommender Systems ◽  
Recommender System ◽  
Commercial Interest ◽  
Big Data Applications ◽  
Random Neural Network

Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.

scholarly journals Contextual Personality-Aware Recommender System Versus Big Data Recommender System

2021 ◽  
pp. 163-173
Author(s):  
Marcin Szmydt
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Factor Model ◽  
Five Factor Model ◽  
Customer Reviews ◽  
System Versus ◽  
Personality Theories

Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

scholarly journals An Optimal Enhancement of the Dynamic Features of Recommender Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 51-55
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender Systems ◽  
Data Analytics ◽  
Recommendation Systems ◽  
Big Data Analytics ◽  
The Internet ◽  
User Requirements ◽  
Dynamic Features ◽  
Dynamic Nature

Recommendation systems come under the domain of Data mining and Big Data analytics. It is useful tool that is used to predict the ratings or preferences of a user from a pool of resources. The preferences of user are dynamic in nature. The immeasurable usage of internet is having a great impact on the way we deal our lives and communicate with each other. As a result, the requirements of user browsing the internet are changing radically. Recommender Systems (RSs) provide a technology that helps users in finding relevant or preferential information among the pool of information using internet. This paper puts forward not only the issues related to the dynamic nature of user’ requirements but also the changes in the systems’ contents. The Recommendation Systems which involves the above stated issues are termed as Dynamic Recommender Systems (DRSs). This paper first defines the concept of DRS and then explores the various parameters that is taken into account in developing a DRS. This paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

scholarly journals Worrying Impact of Artificial Intelligence and Big Data Through the Prism of Recommender Systems

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Ljubiša Bojić ◽  
Maja Zarić ◽  
Simona Žikić
Keyword(s):  
Big Data ◽  
Recommender Systems ◽  
Political Communication ◽  
Data Privacy ◽  
Social Effects ◽  
Low Intensity ◽  
Internet Users ◽  
The Us ◽  
Social Polarization ◽  
Echo Chamber

Transfer from social to semantic web brought us to an era of algorithmic society, placing issues such as privacy, big data and AI in the spotlight. although neutral by their nature, the power of big data algorithms to impact societies became major concern outcoming with fines issued to Facebook in the US. These events were initiated by alleged breaches of data privacy connected to recommender system technology, which can provide individualized content to internet users. This paper seeks to explain recommender systems, while elaborating on their social effects, to conclude that their overall impacts might be increase in retail sales, democratization of advertising, increase in internet addictions, social polarization (echo chamber issue), and improvement of political communication. Also, more research should be deployed into low intensity addictions, as potential outcome of recommender systems, and it should be explored how they affect political participation and democracy.

scholarly journals Improvement of recommender systems considering big data of users’ comments on chosen items

2016 ◽  
Vol 8 (2) ◽  
pp. 882
Author(s):  
Kh. Esfandiari ◽  
A.R. Honarvar ◽  
Sh. Aghamirzadeh
Keyword(s):  
Big Data ◽  
Recommender Systems
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