TraEx: Traditional Arts & Crafts Event Recommendation System with Lifetime Experience for Regional Revitalization

2021 ◽  
Author(s):  
Reo Urata ◽  
Shiori Sasaki
Keyword(s):  
Recommendation System ◽  
Lifetime Experience ◽  
Event Recommendation ◽  
Traditional Arts

scholarly journals Event Recommendation System using Hybrid Method Based on Mobile Device

2021 ◽  
Vol 6 (1) ◽  
pp. 107-116
Author(s):  
Dio Saputra Kudori
Keyword(s):  
Everyday Life ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Mobile Device ◽  
Hybrid Method ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Unique Character ◽  
Event Recommendation

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesn’t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.

scholarly journals Personalized Event Recommendation System

2021 ◽  
Vol 9 (VI) ◽  
pp. 5165-5168
Author(s):  
Vikram Bhavsar
Keyword(s):  
Recommendation System ◽  
Web Browsing ◽  
Meaningful Information ◽  
Event Recommendation

Many times, we receive a large number of notifications about various events, exhibitions, and meetups happening all around us that are irrelevant to us because we simply are not interested in them. Various people have their importance of things that they are interested in and be notified of all these events and from them searching for something that might interest them will take a lot of time and sometimes does not provide any meaningful information. In today’s world, there is no such existing facility that notifies us about the various events that are tailored to our interest strictly based on our web browsing history. Thus, we aim to create a Personalized Event Recommendation System that recommends the events that are sorted according to the user based on his/her interests using their browser history.

scholarly journals Event Cosplay Organizer Online dengan Sistem Rekomendasi Juri menggunakan Perbandingan Metode AHP dan SAW (Studi Kasus pada Kaze Production)

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Ananda Dhammahito Adhe Herlambang ◽  
Julianti Kasih
Keyword(s):  
Human Error ◽  
Recommendation System ◽  
Pop Culture ◽  
Online Media ◽  
Creative Industry ◽  
Great Opportunity ◽  
Event Recommendation ◽  
The Media ◽  
Main Thing ◽  
A Company

Abstract — As a company that moves and involved inside the creative industry such as pop culture, creating and managing events are the main thing that the company works on. By using website as the media for organizing things related to creating and managing events gives not only the company but also the user as member a big grasp to to big things in such a simple way. Besides all the simple things having an online media also gives the easiest way to access it anywhere and creating a better security from human error.(such as losing document files, damage paperwork, etc.)  Having an accessible  media could help not only amateur creator starting to create their first event but also on a professional level where they could manage their work to be more organize and safely. Therefore, in this great opportunity doing final assignment will be used to developed the application of 'Sistem Organizer Online Events Cosplay' based on a company Kaze Prodcution. This application will be enchanced with main features like creating schedule for the events, methods score counting for judge recommendation system, and counting the estimation budget for the events.   Keywords— cosplay, event, recommendation system, organizer, website.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Towards Small-Sized Long Tail Business with the Dual-Directed Recommendation System

2010 ◽  
Vol 130 (2) ◽  
pp. 317-323
Author(s):  
Masakazu Takahashi ◽  
Takashi Yamada ◽  
Kazuhiko Tsuda ◽  
Takao Terano
Keyword(s):  
Recommendation System ◽  
Long Tail
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