scholarly journals Hybrid Recommender System Based on Collaborative Filtering Employing Personal Values-Based User Model

2017 ◽  
Vol 29 (4) ◽  
pp. 628-636
Author(s):  
Shunichi HATTORI ◽  
Ryori MISAWA ◽  
Hiroshi ISHIKAWA ◽  
Yasufumi TAKAMA
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Personal Values ◽  
User Model ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

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

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 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.

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.

Hybrid Recommender System Using Emotional Fingerprints Model

2019 ◽  
Vol 9 (3) ◽  
pp. 48-70
Author(s):  
Anthony Nosshi ◽  
Aziza Saad Asem ◽  
Mohammed Badr Senousy
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Experimental Simulation ◽  
Key Factors ◽  
Easy Task ◽  
Needed Information ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

scholarly journals A Hybrid Movie Recommender System and Rating Prediction Model

2021 ◽  
Vol 3 (3) ◽  
pp. 161-168
Author(s):  
Muhammad Sanwal ◽  
Cafer ÇALIŞKAN
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Support Vector ◽  
Learning Methods ◽  
Long Time ◽  
Data Volume ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

In the current era, a rapid increase in data volume produces redundant information on the internet. This predicts the appropriate items for users a great challenge in information systems. As a result, recommender systems have emerged in this decade to resolve such problems. Various e-commerce platforms such as Amazon and Netflix prefer using some decent systems to recommend their items to users. In literature, multiple methods such as matrix factorization and collaborative filtering exist and have been implemented for a long time, however recent studies show that some other approaches, especially using artificial neural networks, have promising improvements in this area of research. In this research, we propose a new hybrid recommender system that results in better performance. In the proposed system, the users are divided into two main categories, namely average users, and non-average users. Then, various machine learning and deep learning methods are applied within these categories to achieve better results. Some methods such as decision trees, support vector regression, and random forest are applied to the average users. On the other side, matrix factorization, collaborative filtering, and some deep learning methods are implemented for non-average users. This approach achieves better compared to the traditional methods.

Sign in / Sign up

Export Citation Format

Share Document