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Modelling and Data Analysis
2020. Vol. 10, no. 4, 5–16
https://doi.org/10.17759/mda.2020100401
ISSN: 2219-3758 / 2311-9454 (online)

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

 
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Abstract

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.

General Information

Keywords: machine learning, unsupervised learning, recommender systems, object similarity, relation, set.

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex trends: Recommender Systems and Techniques, Information Systems and Technology Applications, Image Retrieval and Classification Techniques

Information about the work in OpenAlex

Number of citations: 0

Topics

Recommender Systems and Techniques

This cluster of papers focuses on the advancements in recommender system technologies, including collaborative filtering, matrix factorization, deep learning, content-based recommendation, web mining, context-aware recommender systems, neural networks, user modeling, and trust-aware recommender systems. The papers cover various techniques and methodologies for improving recommendation accuracy and addressing challenges such as cold start problems and privacy concerns.

Number of works: 74259  |  Total number of citations: 1123302

Topic detailsв OpenAlex

Information Systems and Technology Applications

This cluster of papers focuses on the fundamentals, drivers, and business models of enterprise content management, including topics such as information management, machine learning, predictive modeling, web content management, artificial intelligence, service systems, big data analysis, and digital document management.

Number of works: 16253  |  Total number of citations: 26702

Topic detailsв OpenAlex

Image Retrieval and Classification Techniques

This cluster of papers focuses on shape matching, object recognition, and content-based image retrieval using techniques such as local binary patterns, feature descriptors, and rotation-invariant methods. It also explores the application of these methods in medical imaging, semantic relevance modeling, and machine learning for image annotation.

Number of works: 77193  |  Total number of citations: 1079480

Topic detailsв OpenAlex

Work details in OpenAlex

DOI: https://doi.org/10.17759/mda.2020100401

Published

For citation: Sudakov, V.A., Trofimov, I.A. (2020). The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services. Modelling and Data Analysis, 10(4), 5–16. (In Russ.). https://doi.org/10.17759/mda.2020100401

© Sudakov V.A., Trofimov I.A., 2020

License: CC BY-NC 4.0

References

  1. Melville P., Mooney R., Nagarajan R. Content-Boosted Collaborative Filtering for Improved Recommendations. University of Texas, USA. Proceeding of AAAI-02, Austin, TX, USA, 2002. – 2002. – pp. 187–192.
  2. Jannach D., Zanker M., Felfering A., Friedrich G., Recommender Systems: An Introduction. Cambridge University Press, 2010.
  3. Ricci F., Rokach L., Shapira B., Kantor P. Recommender Systems: Handbook. Springer, 2011.
  4. Linden G., Smith B., York J., Amazon.com recommendations: item-toitem collaborative filtering. Internet Computing – IEEE 7 2003 – pp. 76–80.
  5. Melville P., Mooney R.J., Nagarajan R. Content-boosted collaborative filtering for improved recommendations. Proceedings of the National Conference on Artificial Intelligence – 2002 – pp. 187–192.
  6. Belova K.M., Sudakov V.A. Issledovanie effektivnosti metodov ocenki relevantnosti tekstov [Research of the effectiveness of methods for assessing the relevance of texts]. Preprinty IPM im. M.V. Keldysha = Keldysh Institute preprints, 2020. No 68. 16 p. http://doi.org/10.20948/ prepr-2020–68. (In Russ.).

Information About the Authors

Vladimir A. Sudakov, Doctor of Engineering, Professor of Department 805, Moscow Aviation Institute (MAI), Leading Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-1658-1941, e-mail: sudakov@ws-dss.com

Ivan A. Trofimov, student, Moscow Aviation Institute (MAI), Moscow, Russian Federation, e-mail: trofimovc137@gmail.com

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