Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners' rating

2017 ◽  
Author(s):  
Rudolf Turnip ◽  
Dade Nurjanah ◽  
Dana Sulistyo Kusumo
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Material ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering

scholarly journals A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce

2021 ◽  
Vol 9 (11) ◽  
pp. 635-638
Author(s):  
Dr. C. K. Gomathy
Keyword(s):  
Collaborative Filtering ◽  
Language Processing ◽  
Recommender System ◽  
Matrix Factorization ◽  
Mean Square ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Traditional Approaches ◽  
Content Based Filtering ◽  
Previous User

Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis

Providing Personalized Services to Users in a Recommender System

2015 ◽  
Vol 10 (2) ◽  
pp. 26-48 ◽  
Author(s):  
Olukunle Oduwobi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Ability ◽  
Learning Material ◽  
Electronic Library ◽  
Learning Materials ◽  
Personalized Services ◽  
E Learning ◽  
Personalized Recommender System ◽  
Content Based Filtering

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

scholarly journals Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering

2018 ◽  
Vol 3 (2) ◽  
pp. 11 ◽  
Author(s):  
Sari Rahmawati ◽  
Dade Nurjanah ◽  
Rita Rismala
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Knowledge Based ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Mencari pekerjaan secara online dapat menjadi kendala tersendiri baik pada pada pelamar pekerjaan maupun pada perusahaan yang mencari karyawan. Saat ini banyak pelamar dan perusahaan lebih memilih menggunakan situs rekruitasi online dibandingkan mencari dengan menggunakan mesin pencari. Recommender system menjadi salah satu kelebihan dari website rekruitasi karena website menyimpan informasi profil pekerja lalu memberikan rekomendasi sesuai dengan data yang mereka dapatkan. Pada penelitian ini penulis membuat hybrid recommender system dengan menggabungkan dua teknik yaitu knowledge based recommender system yang akan merekomendasikan pekerjaan berdasarkan profil user, kualifikasi pekerjaan dan pengaruh dari user lain yang akan memberikan rekomendasi pekerjaan berdasarkan user lain yang memiliki kesamaan. Hasil prediksi dari 2 metode itu akan digabungkan berdasarkan social aperture yang diberikan. Berdasarkan hasil pengujian hybrid recommender system memberikan hasil terbaik untuk memprediksi interaksi dan memberikan rekomendasi berdasarkan hasil RMSE dan f1 score.

The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

2021 ◽  
Vol 5 (5) ◽  
pp. 977-983
Author(s):  
Muhammad Johari ◽  
Arif Laksito
Keyword(s):  
Recommender System ◽  
Web Application ◽  
Information Filtering ◽  
Modern Society ◽  
The Internet ◽  
Hybrid Filtering ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Content Based Filtering ◽  
The Web

Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.  

scholarly journals A hybrid recommender system using trust and bi-clustering in order to increase the efficiency of collaborative filtering

2018 ◽  
Vol 15 (2) ◽  
pp. 119-132
Author(s):  
Monireh Hosseini ◽  
Maghsood Nasrollahi ◽  
Ali Baghaei ◽  
◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

scholarly journals Developing a Contextually Personalized Hybrid Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Aysun Bozanta ◽  
Birgul Kutlu
Keyword(s):  
Recommender System ◽  
Real World ◽  
Experimental Evaluation ◽  
User Study ◽  
Contextual Information ◽  
Threshold Values ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Hybrid Recommendation ◽  
Content Based Filtering

It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

scholarly journals Improving Children’s Experience on a Mobile EdTech Platform through a Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Almudena Ruiz-Iniesta ◽  
Luis Melgar ◽  
Alejandro Baldominos ◽  
David Quintana
Keyword(s):  
Collaborative Filtering ◽  
Mobile Devices ◽  
Recommender System ◽  
Experimental Results ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Smile and Learn is an EdTech digital publisher that offers a smart library of close to 100 educational stories and gaming apps for mobile devices aimed at children aged 2 to 10 and their families. Given the complexity of navigating the content, a recommender system was developed. The system consists of two major components: one that generates content recommendations and another that provides explanations and recommendations relevant to parents and educators. The former was implemented as a hybrid recommender system that combines three kinds of recommendations. Among these, we introduce a collaborative filtering adapted to overcome specific limitations associated with younger users. The approach described in this work was tested on real users of the platform. The experimental results suggest that this recommendation model is suitable to suggest apps to children and increase their engagement in terms of usage time and number of games played.

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