scholarly journals An Efficient Deep Learning Approach for Collaborative Filtering Recommender System

2020 ◽  
Vol 171 ◽  
pp. 829-836
Author(s):  
Mohammed Fadhel Aljunid ◽  
Manjaiah Dh
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Approach

scholarly journals Deep learning approach to obtain collaborative filtering neighborhoods

2021 ◽  
Author(s):  
Jesús Bobadilla ◽  
Ángel González-Prieto ◽  
Fernando Ortega ◽  
Raúl Lara-Cabrera
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbors ◽  
Learning Approach ◽  
Localization Algorithm ◽  
Generative Process ◽  
Open Datasets

AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

2021 ◽  
Author(s):  
Cach Nhan Dang ◽  
María N. Moreno ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

scholarly journals Application of Machine Learning in Collaborative Filtering Recommender Systems

2018 ◽  
Vol 7 (4.38) ◽  
pp. 213
Author(s):  
Rajesh Kumar Ojha ◽  
Dr. Bhagirathi Nayak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Intelligent Systems ◽  
Nearest Neighbors ◽  
Learning Technology ◽  
K Nearest Neighbors ◽  
Learning Technique

Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.   

scholarly journals A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector

2021 ◽  
Vol 11 (20) ◽  
pp. 9667
Author(s):  
William Lemus Leiva ◽  
Meng-Lin Li ◽  
Chieh-Yuan Tsai
Keyword(s):  
Deep Learning ◽  
Data Quality ◽  
Recommender System ◽  
Input Data ◽  
Learning Approach ◽  
Two Phase ◽  
The Core ◽  
Machine Learning Methods ◽  
Overall Performance ◽  
Robust Evidence

Research regarding collaborative filtering recommenders has grown fast lately. However, little attention has been paid to discuss how the input data quality impacts the result. Indeed, some review-rating pairs that a user gave to an item are inconsistent and express a different opinion, making the recommendation result biased. To solve the above drawback, this study proposes a two-phase deep learning-based recommender system. Firstly, a sentiment predictor of textual reviews is created, serving as the quality inspector that cleans and improves the input for a recommender. To build accurate predictors, this phase tries and compares a set of deep learning-based algorithms. Secondly, besides only exploiting the consistent review-rating pairs generated by the quality inspector, this phase builds deep learning-based recommender engines. The experiments on a real-world dataset showed the proposed data quality inspector, based on textual reviews, improves the overall performance of recommenders. On average, applying deep learning-based quality inspectors result in an above 6% improvement in RMSE, and more than a 2% boost in F1 score, and accuracy. This is robust evidence to prove the importance of the input data cleaning process in this field. Moreover, empirical evidence indicates the deep learning approach is suitable for modeling the sentiment predictor, and the core recommendation process, clearly outperforming the traditional machine learning methods.

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.

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