scholarly journals The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services

2020 ◽  
Vol 10 (4) ◽  
pp. 5-16
Author(s):  
V.A. Sudakov ◽  
I.A. Trofimov
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Goods And Services ◽  
Complex Models ◽  
Combined Algorithm ◽  
Content Based Filtering

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.

scholarly journals Developing a Scalable and Accurate Job Recommendation System with Distributed Cluster System using Machine Learning Algorithm

2021 ◽  
Vol 7 (2) ◽  
pp. 71-78
Author(s):  
Timothy Dicky ◽  
Alva Erwin ◽  
Heru Purnomo Ipung
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Hadoop Mapreduce ◽  
Job Recommendation ◽  
Hadoop Cluster ◽  
Time Required ◽  
Distributed Cluster

The purpose of this research is to develop a job recommender system based on the Hadoop MapReduce framework to achieve scalability of the system when it processes big data. Also, a machine learning algorithm is implemented inside the job recommender to produce an accurate job recommendation. The project begins by collecting sample data to build an accurate job recommender system with a centralized program architecture. Then a job recommender with a distributed system program architecture is implemented using Hadoop MapReduce which then deployed to a Hadoop cluster. After the implementation, both systems are tested using a large number of applicants and job data, with the time required for the program to compute the data is recorded to be analyzed. Based on the experiments, we conclude that the recommender produces the most accurate result when the cosine similarity measure is used inside the algorithm. Also, the centralized job recommender system is able to process the data faster compared to the distributed cluster job recommender system. But as the size of the data grows, the centralized system eventually will lack the capacity to process the data, while the distributed cluster job recommender is able to scale according to the size of the data.

scholarly journals Job Recommendation System Implementation in Python vs. C++

2019 ◽  
Vol 8 (4) ◽  
pp. 2299-2302
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Programming Language ◽  
Mathematical Description ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Learning Problem ◽  
Wide Range ◽  
Job Recommendation

Implementing a machine learning algorithm gives you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for the transformations on those structures. There are numerous micro-decisions required when implementing a machine learning algorithm, like Select programming language, Select Algorithm, Select Problem, Research Algorithm, Unit Test and these decisions are often missing from the formal algorithm descriptions. The notion of implementing a job recommendation (a classic machine learning problem) system using to two algorithms namely, KNN [3] and logistic regression [3] in more than one programming language (C++ and python) is introduced and we bring here the analysis and comparison of performance of each. We specifically focus on building a model for predictions of jobs in the field of computer sciences but they can be applied to a wide range of other areas as well. This paper can be used by implementers to deduce which language will best suite their needs to achieve accuracy along with efficiency We are using more than one algorithm to establish the fact that our finding is not just singularly applicable.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

scholarly journals Comparative Study of Machine Learning Algorithms for Recommendation System

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Sonam Singh ◽  
◽  
Kriti Srivastva ◽  
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Research Work ◽  
Previous History ◽  
Machine Learning Algorithms ◽  
The Given ◽  
User History

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

Improving the Shilling Attack Detection in Recommender Systems Using an SVM Gaussian Mixture Model

2019 ◽  
Vol 18 (01) ◽  
pp. 1950011 ◽  
Author(s):  
Jasem M. Alostad
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Recommender System ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Machine Learning Algorithm

With recent advances in e-commerce platforms, the information overload has grown due to increasing number of users, rapid generation of data and items in the recommender system. This tends to create serious problems in such recommender systems. The increasing features in recommender systems pose some new challenges due to poor resilience to mitigate against vulnerable attacks. In particular, the recommender systems are more prone to be attacked by shilling attacks, which creates more vulnerability. A recommender system with poor detection of attacks leads to a reduced detection rate. The performance of the recommender system is thus affected with poor detection ability. Hence, in this paper, we improve the resilience against shilling attacks using a modified Support Vector Machine (SVM) and a machine learning algorithm. The Gaussian Mixture Model is used as a machine learning algorithm to increase the detection rate and it further reduces the dimensionality of data in recommender systems. The proposed method is evaluated against several result metrics, such as the recall rate, precision rate and false positive rate between different attacks. The results of the proposed system are evaluated against probabilistic recommender approaches to demonstrate the efficacy of machine learning language in recommender systems.

scholarly journals Implementation of Movie Recommendation System Using Machine Learning

2020 ◽  
pp. 587-593
Author(s):  
S. Sridevi ◽  
Celeste Murnal
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Recommendation System ◽  
Service Providers ◽  
Learning Algorithm ◽  
Web Based ◽  
Ensemble Learning Algorithm ◽  
Movie Recommendation ◽  
High Possibility ◽  
Content Based Filtering

As world is evolving, similarly people's desire, trend, interests are also changing. Same way even in the field of movies, people want to watch the movies according to their interest. Many web-based movie service providers have emerged and to increase their business and popularity, they want to keep their subscribers entertained. To improve their business, the service provider should recommend movies which users might like, so that they might watch another movie and be entertained. By doing this there is high possibility that customers will periodically renew the web-based movie service provider application. The objective of this project is to implement the machine learning based movie recommendation system which can recommend the movies to the users based on their interest and ratings. To achieve this, content-based filtering is used to recommend movie based on movie-movie similarity, collaborative based filtering is used to compute features based on user information and movie information. The proposed system uses the new ensemble learning algorithm, XGBoost algorithm to improve the performance. The results show that the proposed system is effective for movie recommendation and the system minimizes the root mean square error (RMSE).

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

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