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Modelling and Data Analysis
2020. Vol. 10, no. 4, 17–30
https://doi.org/10.17759/mda.2020100402
ISSN: 2219-3758 / 2311-9454 (online)

Prediction the Probability of Purchases Recommended Items

 
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Abstract

This paper discusses various methods for improving recommendation systems. A comparative analysis of two models for solving classification problems is performed: random forest and CatBoostClassifier. The research was performed on the data of the purchase history of Ozon customers. Standard methods that are often used in recommendation systems were used. We implemented collaborative filtering methods, cosine similarity of products from customer views per site visit, and similarity of text data. To evaluate the results, we used special metrics that evaluate the quality of predictions of the first k objects from the recommendations: Mean average precision (map@K) and Recall at K (recall@k). When generating additional features based on various methods that reveal the similarity of objects, an increase in the quality of model forecasts is noted. The CatBoostClassifier model showed the best results.

General Information

Keywords: recommendation systems, machine learning, binary classification, collaborative filtering methods, cosine similarity, map@K, recall@k

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex topics: Data Mining Algorithms and Applications, Advanced Text Analysis Techniques, Customer churn and segmentation

Information about the work in OpenAlex

Number of citations: 0

Topics

Data Mining Algorithms and Applications

This cluster of papers covers a wide range of topics in data mining, including frequent pattern mining, association rule mining, sequential pattern mining, machine learning, decision trees, interestingness measures, high utility itemsets, temporal data mining, and knowledge discovery.

Number of works: 81472  |  Total number of citations: 1606034

Topic detailsв OpenAlex

Advanced Text Analysis Techniques

This cluster of papers focuses on the automatic extraction of keywords from textual data using various techniques such as graph-based methods, unsupervised approaches, and neural networks. The research explores the application of linguistic knowledge and statistical information to improve the accuracy of keyword extraction from documents.

Number of works: 43765  |  Total number of citations: 481411

Topic detailsв OpenAlex

Customer churn and segmentation

This cluster of papers focuses on customer equity management and prediction, utilizing data mining, machine learning, and segmentation techniques to understand customer churn, lifetime value, and profitability. It explores the impact of marketing strategies on customer retention and financial performance in various industries, particularly in telecommunications.

Number of works: 30490  |  Total number of citations: 152057

Topic detailsв OpenAlex

Work details in OpenAlex

DOI: https://doi.org/10.17759/mda.2020100402

Published

For citation: Parfenov, P.A., Timofeeva, A.A., Sologub, G.B., Alekseychuk, A.S. (2020). Prediction the Probability of Purchases Recommended Items. Modelling and Data Analysis, 10(4), 17–30. (In Russ.). https://doi.org/10.17759/mda.2020100402

© Parfenov P.A., Timofeeva A.A., Sologub G.B., Alekseychuk A.S., 2020

License: CC BY-NC 4.0

References

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  3. Brent Smith and Greg Linden. Two Decades of Recommender Systems at Amazon.com // the IEEE Computer Society, 2017, pp. 10–17.
  4. Carlos A. Gomez-Uribe and Neil Hunt. The Netflix Recommender System: Algorithms, Business Value, and Innovation // ACM Transactions on Management Information Systems, Vol. 6, No. 4, Article 13, 2015, pp. 6–7.
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  11. Gunnar Schröder, Maik Thiele, Wolfgang Lehner. Setting Goals and Choosing Metrics for Recommender System Evaluations, 2011 P. 8.
  12. Ziwei Zhu, Jianling Wang, James Caverlee // Improving Top-K Recommendation via Joint Collaborative Autoencoders, IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4 License, 2019. P. 7.
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Information About the Authors

Pavel A. Parfenov, Moscow Aviation Institute (National Research University), Russian Federation, ORCID: https://orcid.org/0000-0001-5995-347X, e-mail: pentalbymf@mail.ru

Alena A. Timofeeva, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-7043-3715, e-mail: alena195101@yandex.ru

Gleb B. Sologub, Candidate of Science (Physics and Matematics), Associate Professor of the Department of Mathematical Cybernetics of Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5657-4826, e-mail: glebsologub@ya.ru

Andrey S. Alekseychuk, Candidate of Science (Physics and Matematics), Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University) (MAI), Associate Professor of the Department of Digital Education, Moscow State University of Psychology and education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-4167-8347, e-mail: alexejchuk@gmail.com

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