Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting

2020 ◽  
Vol 270 ◽  
pp. 115144 ◽  
Author(s):  
Wenqiang Li ◽  
Guangcai Gong ◽  
Houhua Fan ◽  
Pei Peng ◽  
Liang Chun
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Learning Strategy ◽  
User Preferences ◽  
Cooling Load ◽  
Meta Learning

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 Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

2020 ◽  
Vol 2 (95) ◽  
pp. 21-27
Author(s):  
S. F. Chalyi ◽  
V. O. Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Selection Process ◽  
Temporal Patterns ◽  
User Preferences ◽  
Data Sets ◽  
Goods And Services ◽  
Temporal Relationships ◽  
Sequential Selection ◽  
System User ◽  
Alternative Choice

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

Personalized Real-Time Virtual Tours in Places With Cultural Interest

2020 ◽  
pp. 802-820
Author(s):  
Emmanouil Skondras ◽  
Konstantina Siountri ◽  
Angelos Michalas ◽  
Dimitrios D. Vergados
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Fuzzy Topsis ◽  
Analytic Network Process ◽  
User Preferences ◽  
Web Ontology Language ◽  
Ontology Language ◽  
Cultural Sites ◽  
Network Process ◽  
Cultural Interest

Virtual tours using drones enhance the experience the users perceive from a place with cultural interest. Drones equipped with 360o cameras perform real-time video streaming of the cultural sites. The user preferences about each monument type should be considered in order to decide the appropriate flying route for the drone. This article describes a scheme for supporting personalized real-time virtual tours at sites with cultural interest using drones. The user preferences are modeled using the MPEG-21 and the MPEG-7 standards, while Web Ontology Language (OWL) ontologies are used for metadata structure and semantics. The Metadata-Aware Analytic Network Process (MANP) algorithm is proposed in order to weigh the user preferences for each monument type. Subsequently, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS) algorithm ranks the candidate heritage routes. Finally, after each virtual tour, the user preferences metadata are updated in order in order the scheme to continuously learn about the user preferences.

A Survey on Recent Recommendation Systems for the Tourism Industry

2020 ◽  
pp. 205-237
Author(s):  
S. Ranjith ◽  
P. Victer Paul
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Tourism Industry ◽  
User Preferences ◽  
Reference List ◽  
Current State ◽  
Recent Recommendation ◽  
Important Field ◽  
Preference Criteria

Data mining is an important field that derives insights from the data and recommendation systems. Recommendation systems have become common in recent years in the field of tourism. These are widely used as a tool that can input various selection criteria and user preferences and yields travel recommendations to tourists. User's style and preferences should be constructed accurately so as to supply most relevant suggestions. Researchers proposed various types of tourism recommendation systems (TRS) in order to improve the accuracy and user satisfaction. In this chapter, the authors studied the current state of tourism recommendation system models and discussed their preference criteria. As a part of that, the authors studied various important preference factors in TRS and categorized them based on their likeness. This chapter reports TRS model future directions and compiles a comprehensive reference list to assist researchers.

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