THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM
The problem of constructing recommendations for electronic commerce systems under conditions of cyclic cold start is investigated. This problem occurs with the constant change of user interests over the period of use of the reference subsystem. Existing approaches to forming recommendations in a col...
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| Format: | Article |
| Language: | English |
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National Technical University Kharkiv Polytechnic Institute
2018-12-01
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| Series: | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
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| Online Access: | http://samit.khpi.edu.ua/article/view/2079-0023.2018.44.11 |
| _version_ | 1797758623370182656 |
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| author | Volodymyr Oleksandrovich Leshchynskyi Irina Oleksandrivna Leshchynska |
| author_facet | Volodymyr Oleksandrovich Leshchynskyi Irina Oleksandrivna Leshchynska |
| author_sort | Volodymyr Oleksandrovich Leshchynskyi |
| collection | DOAJ |
| description | The problem of constructing recommendations for electronic commerce systems under conditions of cyclic cold start is investigated. This problem occurs with the constant change of user interests over the period of use of the reference subsystem. Existing approaches to forming recommendations in a cold start are based on the gradual accumulation of consumer information and therefore do not provide relevant recommendations in the event of cyclical changes in their activities and interests. To address this problem, it is proposed to take into account the aspect of changing user interests in relation to goods and services in time. The purpose of this work is to clarify the principles of active training to build recommendations in the changing consumer interests in order to provide a step-by-step refinement of personal recommendations to "cold" consumers. The obtained results contain a detailed task of forming recommendations, and the principles of active training are specified. The key feature of the cyclic cold start in the reference system is distinguished, which is the limited period during which the information about the customer of the electronic commerce system can be supplemented and refined. This feature makes it necessary to take into account the aspect of time when forming recommendations on the choice of goods and services. The problem of forming recommendations in the conditions of cyclic cold start is formulated as a task of iterative addition and refinement of the data of the new "cold" user by the patterns of the most common cycles of consumer behavior followed by the use of collaborative filtering of the refined data for the formulation of recommendations. The principles of active training for cyclic cold start conditions based on the use of typical sequence sequences of the user in time are supplemented. These principles allow you to adjust the input data for a "cold" user using a heuristic strategy that takes into account changes in patterns of consumer behavior. The patterns of behavior reflect cyclical changes in consumers' interest in the products and services offered by the e-commerce system. |
| first_indexed | 2024-03-12T18:32:42Z |
| format | Article |
| id | doaj.art-24b6da3949eb4454bb4be172c3d73c9a |
| institution | Directory Open Access Journal |
| issn | 2079-0023 2410-2857 |
| language | English |
| last_indexed | 2024-03-12T18:32:42Z |
| publishDate | 2018-12-01 |
| publisher | National Technical University Kharkiv Polytechnic Institute |
| record_format | Article |
| series | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
| spelling | doaj.art-24b6da3949eb4454bb4be172c3d73c9a2023-08-02T08:11:39ZengNational Technical University Kharkiv Polytechnic InstituteВісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології2079-00232410-28572018-12-01132044667110.20998/2079-0023.2018.44.112079-0023.2018.44.11THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEMVolodymyr Oleksandrovich LeshchynskyiIrina Oleksandrivna LeshchynskaThe problem of constructing recommendations for electronic commerce systems under conditions of cyclic cold start is investigated. This problem occurs with the constant change of user interests over the period of use of the reference subsystem. Existing approaches to forming recommendations in a cold start are based on the gradual accumulation of consumer information and therefore do not provide relevant recommendations in the event of cyclical changes in their activities and interests. To address this problem, it is proposed to take into account the aspect of changing user interests in relation to goods and services in time. The purpose of this work is to clarify the principles of active training to build recommendations in the changing consumer interests in order to provide a step-by-step refinement of personal recommendations to "cold" consumers. The obtained results contain a detailed task of forming recommendations, and the principles of active training are specified. The key feature of the cyclic cold start in the reference system is distinguished, which is the limited period during which the information about the customer of the electronic commerce system can be supplemented and refined. This feature makes it necessary to take into account the aspect of time when forming recommendations on the choice of goods and services. The problem of forming recommendations in the conditions of cyclic cold start is formulated as a task of iterative addition and refinement of the data of the new "cold" user by the patterns of the most common cycles of consumer behavior followed by the use of collaborative filtering of the refined data for the formulation of recommendations. The principles of active training for cyclic cold start conditions based on the use of typical sequence sequences of the user in time are supplemented. These principles allow you to adjust the input data for a "cold" user using a heuristic strategy that takes into account changes in patterns of consumer behavior. The patterns of behavior reflect cyclical changes in consumers' interest in the products and services offered by the e-commerce system.http://samit.khpi.edu.ua/article/view/2079-0023.2018.44.11рекомендаційні системиранжування результатівхолодний стартколаборативна фільтраціяформування рекомендацій |
| spellingShingle | Volodymyr Oleksandrovich Leshchynskyi Irina Oleksandrivna Leshchynska THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології рекомендаційні системи ранжування результатів холодний старт колаборативна фільтрація формування рекомендацій |
| title | THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM |
| title_full | THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM |
| title_fullStr | THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM |
| title_full_unstemmed | THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM |
| title_short | THE USE OF ACTIVE LEARNING IN A SITUATION OF A CYCLICAL COLD START OF THE RECOMMENDER SYSTEM |
| title_sort | use of active learning in a situation of a cyclical cold start of the recommender system |
| topic | рекомендаційні системи ранжування результатів холодний старт колаборативна фільтрація формування рекомендацій |
| url | http://samit.khpi.edu.ua/article/view/2079-0023.2018.44.11 |
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