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HYBRID COLLABORATIVE FILTERING ALGORITHMS FOR PRODUCT GROUP RECOMMENDATIONS BASED ON HETEROGENEOUS USER DATA

Verbova Maria Andreevna  (Moscow Polytechnic University)

Podolnyy Vladimir Alexandrovich  (Moscow Polytechnic University)

Suvorov Stanislav Vadimovich  (Candidate of Economic Sciences, Professor, Moscow Polytechnic University)

With the rapid growth of e-commerce, the ability to hold the user's attention and provide relevant personalized content is becoming an important success factor. The purpose of this work is to develop and experimentally validate a hybrid recommendation system algorithm for a marketplace that solves the problems of incomplete interaction history and disparate metadata. The proposed solution combines two classical approaches: collaborative filtering based on matrix decompositions (Alternating Least Squares) to identify hidden patterns in implicit interactions and a modified popularity-based recommendation method that takes into account the individual user story.

Keywords:recommendation systems, hybrid algorithm, collaborative filtering, Alternating Least Squares.
 

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Citation link:
Verbova M. A., Podolnyy V. A., Suvorov S. V. HYBRID COLLABORATIVE FILTERING ALGORITHMS FOR PRODUCT GROUP RECOMMENDATIONS BASED ON HETEROGENEOUS USER DATA // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№01. -С. 64-69 DOI 10.37882/2223-2966.2026.01.11
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