scholarly journals Um Modelo para Auxiliar a Descoberta e Classificação de Conteúdo para Ambientes Virtuais de Aprendizagem

2020 ◽  
Author(s):  
Rafael Martins Feitosa ◽  
Cleyton Aparecido Dim ◽  
Marcelle Pereira Mota ◽  
Jefferson Magalhães de Morais ◽  
Raimundo Viegas Junior ◽  
...  
Keyword(s):  
Recommendation System ◽  
Formal Training ◽  
Amount Of Information ◽  
Proposed Model ◽  
Select Material ◽  
Hybrid Recommendation ◽  
Feedback Evaluation

The massive amount of information currently available on the Internetmakes it difficult for teachers to curate quality educationalcontent or to select material for self-regulated study by students.Aiming to facilitate these steps in the teaching and/or learningprocess this article presents an approach to assist the discovery ofeducational content from the hybrid recommendation system andlater classification from the feedback evaluation with sentimentanalysis techniques trained with the Re-Li corpus. This paper describesthe proposed model, the implementation of a prototype andits application in non-formal training involving 13 participants.

Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

2021 ◽  
Vol 25 (4) ◽  
pp. 1013-1029
Author(s):  
Zeeshan Zeeshan ◽  
Qurat ul Ain ◽  
Uzair Aslam Bhatti ◽  
Waqar Hussain Memon ◽  
Sajid Ali ◽  
...  
Keyword(s):  
Recommendation System ◽  
Sources Of Information ◽  
Multiple Features ◽  
Recommendation Algorithms ◽  
Label Correlations ◽  
Feature Based ◽  
Multicriteria Model ◽  
Implicit And Explicit ◽  
Hybrid Recommendation ◽  
Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

scholarly journals Hybrid Recommendation System with Review Helpfulness Features

IARJSET ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 148-151
Author(s):  
Miss. Jadhav Monika ◽  
Mrs. Kakade Shital P
Keyword(s):  
Recommendation System ◽  
Review Helpfulness ◽  
Hybrid Recommendation

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

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