Dynamic Updating of Association Rules in Intelligent E-Commerce Recommendation Systems
DOI:
https://doi.org/10.47451/inn2025-01-02Keywords:
e-commerce system, intelligent recommendation system, association rule, support, confidence, Арrіоrі algorithm, dynamic update of associative rules, incremental association rule miningAbstract
The accumulation of large volumes of digital content in e-commerce necessitates implementing intelligent recommendation systems in their web platforms, which contribute to increasing financial profits by enhancing the efficiency of e-commerce. Among the methods used for generating forecasts in recommendation systems, Association Rule Mining (ARM) is widely applied. ARM uncovers hidden relationships between objects in large datasets. Many algorithms have been proposed for updating association rules in recommendation systems using incremental association rule mining. This approach involves rerunning the search algorithm on a modified transaction database instead of the entire database. However, dynamic updating of association rules in e-commerce systems remains an unsolved task that requires further development. The study object is the process of updating association rules in e-commerce recommendation systems. The study aims to develop and describe a method for dynamically updating association rules in an e-commerce recommendation system, which is implemented using the Apriori algorithm. The Apriori algorithm is based on finding association rules for frequent itemsets and is static and highly complex. In this work, dynamic updating of found association rules to ensure their relevance is proposed through periodic scanning of a portion of the database that contains transaction records from the past three months. The database is updated by adding new products and removing those that have been discontinued during this period. The proposed approach was implemented in actual operational conditions in an e-commerce system engaged in the retail sale of animal supplements. The study of the effectiveness of the developed intelligent recommendation system showed that its use was accompanied by an increase in the number of products sold, the average purchase value, and the conversion rate.
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