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.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

scholarly journals Enhanced Hybrid Recommender System using Social Friend Network

2014 ◽  
Vol 10 (4) ◽  
pp. 2023-2031
Author(s):  
Shalmali A. Patil ◽  
Reena Pagare
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Recommender System ◽  
Social Relationship ◽  
Information Overload ◽  
Great Success ◽  
Traditional System ◽  
Use Of Data ◽  
The Social ◽  
Hybrid Recommender

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals A Hybrid Recommender System Based on User-Recommender Interaction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Heng-Ru Zhang ◽  
Fan Min ◽  
Xu He ◽  
Yuan-Yuan Xu
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Hybrid Algorithm ◽  
Nearest Neighbor ◽  
User Behavior ◽  
User Choice ◽  
User Request ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Diversity Metrics

Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommendsNitems to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random andk-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

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.

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