scholarly journals Eventera: Real-Time Event Recommendation System from Massive Heterogeneous Online Media

2014 ◽  
Author(s):  
Dongyeop Kang ◽  
Donggyun Han ◽  
Nahea Park ◽  
Sangtae Kim ◽  
U. Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Online Media ◽  
Event Recommendation

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.

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

A Cross-Platform Blood Donation Application with a Real-Time, Intelligent, and Rational Recommendation System

2021 ◽  
Author(s):  
Md Rafat Jamader Maraz ◽  
Rashik Rahman ◽  
Md. Mehedi Ul Hasnain ◽  
Hasan Murad
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Blood Donation ◽  
Cross Platform

scholarly journals Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

2019 ◽  
pp. 38-44
Author(s):  
Selvi C ◽  
Keerthana D
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Large Scale ◽  
Recommendation System ◽  
Important Research ◽  
Taxi Drivers ◽  
Charging Station ◽  
Gps Trajectories ◽  
Charging Mechanism ◽  
Supply Level

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

scholarly journals Towards building real-time, convenient route recommendation system for public transit

2016 ◽  
Author(s):  
Garvita Bajaj ◽  
Rachit Agarwal ◽  
Georgios Bouloukakis ◽  
Pushpendra Singh ◽  
Nikolaos Georgantas ◽  
...  
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Public Transit ◽  
Convenient Route ◽  
Route Recommendation

scholarly journals Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
Author(s):  
Faisal Mehmood ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Big Data ◽  
Real Time ◽  
Recommendation System ◽  
Tourism Industry ◽  
Jeju Island ◽  
Third Party ◽  
User Preference ◽  
Optimal Route ◽  
Route Recommendation ◽  
Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

scholarly journals Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

2020 ◽  
Vol 2674 (6) ◽  
pp. 45-59
Author(s):  
Weiran Yao ◽  
Sean Qian
Keyword(s):  
Real Time ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Time Window ◽  
Traffic Prediction ◽  
Incident Management ◽  
Signal Timing ◽  
Late Response ◽  
Main Question ◽  
Real Time Traffic

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makes recommendations an average of 22.5 min lead time ahead of Waze alerts. The results suggest that this framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.

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