scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

Big Data in manufacturing systems engineering – close up on a machine tool

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Christian Bauer ◽  
Zaigham-Faraz Siddiqui ◽  
Manuel Beuttler ◽  
Klaus Bauer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Systems Engineering ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
The Internet ◽  
New Approach ◽  
New Challenges ◽  
The Internet Of Things

AbstractWith the increasing connectivity of devices, the amount of data that is recorded and ready for analysis is growing correspondingly. This is also the case for shop floors in flexible sheet metal handling and production. With the growing need for flexibility in production, the availability of machine tools is imminent. This paper shows different approaches that a classical manufacturing systems company such as TRUMPF takes in applying data mining techniques to address the new challenges which come with the Internet of things. In addition to classical methods, a new approach is introduced that does not need any alteration of the machine or its interfaces.

scholarly journals Personalized Recommender System for Calculus using Content-Based Filtering Approach

2018 ◽  
Vol 7 (3.15) ◽  
pp. 110 ◽  
Author(s):  
Noor Latiffah Adam ◽  
Muhammad Alif Zulkafli ◽  
Shaharuddin Cik Soh ◽  
Nor Ashikin Mohamad Kamal ◽  
Nordin Abu Bakar
Keyword(s):  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Basic Calculus ◽  
Personalized Recommender System ◽  
Many Sources ◽  
Content Based Filtering ◽  
Human Kind ◽  
The Web ◽  
Filtering Approach

In this millennial age, Internet is becoming essential to human kind. Along with the growth of Internet users, information is also becoming huge and starting to cause difficulties to find the relevant contents. Thus, the recommender system was introduced. It helps the user to suggest the items based on the user’s preferences. This system could help the students as Calculus is one of the tough subjects feared by most students. Credits given to the technology as many sources on the web can provide tutorials, working examples and solutions on the subjects. However, there are too many of them. Students had to make a few selections, which one can fulfil their needs of specific calculus topics. The personalized recommender system developed was a content-based filtering recommender system with its own scraping engine to collect the sources from the Internet which focuses on the basic Calculus topics. The system and engine were constructed by using Flask framework together with its relevant libraries. 

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.

Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

2021 ◽  
Vol 5 (5) ◽  
pp. 977-983
Author(s):  
Muhammad Johari ◽  
Arif Laksito
Keyword(s):  
Recommender System ◽  
Web Application ◽  
Information Filtering ◽  
Modern Society ◽  
The Internet ◽  
Hybrid Filtering ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering ◽  
The Web

Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.  

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

scholarly journals The future of film-making: Data-driven movie-making techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 167-174
Author(s):  
Nadide Gizem Akgülgil Mutlu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Driven ◽  
Art World ◽  
Human Beings ◽  
Digital Era ◽  
Production And Distribution ◽  
Pros And Cons ◽  
Film Making

Since the term ‘big data’ came to the scene, it has left almost no industry unaffected. Even the art world has taken advantage of the benefits of big data. One of the latest art forms, cinema, eventually started using analytics to predict their audience and their tastes through data mining. In addition to online platforms like Netflix, Amazon Prime and many more, which act on a different basis, the industry itself evolved to a new phase that uses AI in pre-production, production, post-production and distribution phases. This paper researches software, such as Cinelytic, ScriptBook and LargoAI, and their working strategies to understand the role of directors and producers in the age of the digital era in film-making. The research aims to find answers to the capabilities of data-driven movie-making techniques and, accordingly, it makes a number of predictions about the role of human beings in the production of an artwork and analyses the role of the software. The research also investigates the pros and cons of using big data in the film-making industry.   Keywords: Artificial intelligence, cinema, data mining, film-making.

scholarly journals The problem of analysis of big web data and the use of data mining technology for processing and searching patterns in big web data on a practical example

2019 ◽  
Vol 23 (2) ◽  
pp. 42-49 ◽  
Author(s):  
K. V. Mulyukova ◽  
V. M. Kureichik
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Big Data ◽  
Data Processing ◽  
Practical Problem ◽  
The Internet ◽  
Web Data ◽  
Mining Technology ◽  
C Programming Language ◽  
C Programming

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

Art Innovative Systems for Value Tagging

2021 ◽  
pp. 1163-1174
Author(s):  
Laurel Powell ◽  
Anna Gelich ◽  
Zbigniew W. Ras
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Data Analytics ◽  
Word Of Mouth ◽  
Big Data Analytics ◽  
Art Market ◽  
Fine Art ◽  
The Internet ◽  
User Needs ◽  
Personalized Recommender System

Prices of artworks are rather arbitrary. Artists use word of mouth and galleries to learn about pricing. Also, the professionals in the art market are searching the internet for information about prices of comparable artworks to the ones they plan to sell, but it is not very helpful. Existing systems do not use data analytics but human experts to evaluate fine art pieces and make recommendations. The system discussed in this article, called ArtIST, is based on big data analytics. Using the artist's name, appraisal of the piece of art is done by a personalized recommender system built from the data describing similar artists and similar art pieces including information about their sales. To evaluate an art piece using ArtIST, the user needs to submit the same information about the work as is required by existing art appraisal tools or websites.

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