Optimized machine learning based collaborative filtering (OMLCF) recommendation system in e-commerce

Recommendation systems are class of information filter applications whose main goal is to provide personalized recommendations. The main goal of the research was to compare two ways of creating personalized recommendations. The recommendation system was built on the basis of a content-based cognitive filtering method and on the basis of a collaborative filtering method based on user ratings. The conclusions of the research show the advantages and disadvantages of both methods.

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Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽

10.3390/sym12091566 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1566 ◽

Cited By ~ 2

Author(s):

Zeinab Shahbazi ◽

Debapriya Hazra ◽

Sejoon Park ◽

Yung Cheol Byun

Keyword(s):

Machine Learning ◽

South Korea ◽

Collaborative Filtering ◽

Mean Square Error ◽

Prediction Accuracy ◽

Recommendation System ◽

Recommendation Systems ◽

Gradient Boosting ◽

Mean Square ◽

Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

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Comparative Study of Machine Learning Algorithms for Recommendation System

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst12495 ◽

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.

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Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2019.175507 ◽

2019 ◽

Vol 4 (2 (100)) ◽

pp. 6-28 ◽

Cited By ~ 1

Author(s):

Vasyl Lytvyn ◽

Victoria Vysotska ◽

Viktor Shatskykh ◽

Ihor Kohut ◽

Oksana Petruchenko ◽

...

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Personal Needs

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Machine Learning based Efficient Recommendation System for Book Selection using User based Collaborative Filtering Algorithm

2020 Fourth International Conference on Inventive Systems and Control (ICISC) ◽

10.1109/icisc47916.2020.9171222 ◽

2020 ◽

Author(s):

Madhuri Kommineni ◽

P. Alekhya ◽

T. Mohana Vyshnavi ◽

V. Aparna ◽

K Swetha ◽

...

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Book Selection ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm

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Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500619 ◽

2021 ◽

pp. 1-22

Author(s):

Celestine Iwendi ◽

Ebuka Ibeke ◽

Harshini Eggoni ◽

Sreerajavenkatareddy Velagala ◽

Gautam Srivastava

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Absolute Error ◽

Learning Model ◽

Text Recall ◽

Machine Learning Model ◽

Similarity Algorithm ◽

Recommendation Accuracy ◽

Better Than

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

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Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System

Journal of Physics Conference Series ◽

10.1088/1742-6596/2007/1/012032 ◽

2021 ◽

Vol 2007 (1) ◽

pp. 012032

Author(s):

Mahesh Kumar Singh ◽

Om Prakash Rishi ◽

Akhilesh Kumar Singh ◽

Pushpendra Singh ◽

Pushpa Choudhary

Keyword(s):

Machine Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Knowledge Based

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

Modelling and Data Analysis ◽

10.17759/mda.2020100401 ◽

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.

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