scholarly journals A genetic-based pairwise trip planner recommender system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

AbstractThe massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

scholarly journals A Genetic-Based Pairwise trip Planner Recommender System

2021 ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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. 

MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health

2020 ◽  
Author(s):  
Darius A. Rohani ◽  
Andrea Quemada Lopategui ◽  
Nanna Tuxen ◽  
Maria Faurholt-Jepsen ◽  
Lars V. Kessing ◽  
...  
Keyword(s):  
Mental Health ◽  
Recommender System ◽  
Behavioral Activation ◽  
Personalized Recommender System

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

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